






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  snake_case__      (							unittest.TestCase   ):
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=13    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=99    ,     lowerCAmelCase__=24    ,     lowerCAmelCase__=32    ,     lowerCAmelCase__=5    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=37    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=5_12    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=0.0_2    ,     lowerCAmelCase__=4    ,     )							->   Dict:
							__magic_name__    :     Dict     		=      parent
							__magic_name__    :     Dict     		=      batch_size
							__magic_name__    :     Optional[int]     		=      seq_length
							__magic_name__    :     List[str]     		=      is_training
							__magic_name__    :     Union[str, Any]     		=      use_attention_mask
							__magic_name__    :     str     		=      use_token_type_ids
							__magic_name__    :     Optional[int]     		=      use_labels
							__magic_name__    :     str     		=      vocab_size
							__magic_name__    :     Optional[int]     		=      hidden_size
							__magic_name__    :     Dict     		=      embedding_size
							__magic_name__    :     List[Any]     		=      num_hidden_layers
							__magic_name__    :     Tuple     		=      num_attention_heads
							__magic_name__    :     Dict     		=      intermediate_size
							__magic_name__    :     Dict     		=      hidden_act
							__magic_name__    :     Union[str, Any]     		=      hidden_dropout_prob
							__magic_name__    :     int     		=      attention_probs_dropout_prob
							__magic_name__    :     Any     		=      max_position_embeddings
							__magic_name__    :     Optional[Any]     		=      type_vocab_size
							__magic_name__    :     Tuple     		=      type_sequence_label_size
							__magic_name__    :     Union[str, Any]     		=      initializer_range
							__magic_name__    :     int     		=      num_choices
	def     __magic_name__       (       self				)							->   Optional[Any]:
							__magic_name__    :     int     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)

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

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

							__magic_name__    :     List[str]     		=      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     __magic_name__       (       self				)							->   Any:
							__magic_name__    :     Any     		=      self.prepare_config_and_inputs()
							__magic_name__    :     Dict     		=      config_and_inputs
							__magic_name__    :     Any     		=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict





@require_flax
class  snake_case__      (							_lowerCAmelCase		,						unittest.TestCase   ):
	lowercase__		:  Dict							   =					True

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







	@slow
	def     __magic_name__       (       self				)							->   Optional[int]:
							for model_class_name in self.all_model_classes:
													if model_class_name == FlaxElectraForMaskedLM:
																			__magic_name__    :     Dict     		=      model_class_name.from_pretrained("""google/electra-small-generator"""				)
													else:
																			__magic_name__    :     int     		=      model_class_name.from_pretrained("""google/electra-small-discriminator"""				)
													__magic_name__    :     List[str]     		=      model(np.ones((1, 1)				)				)
													self.assertIsNotNone(lowerCAmelCase__				)








from typing import Literal

__magic_name__:  List[Any]  	=			"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     int     		=      LETTERS if mode == """decrypt""" else key
						__magic_name__    :     Optional[Any]     		=      key if mode == """decrypt""" else LETTERS
						__magic_name__    :     Optional[Any]     		=      """"""
						# loop through each symbol in the message
						for symbol in message:
												if symbol.upper() in chars_a:
																		# encrypt/decrypt the symbol
																		__magic_name__    :     Optional[Any]     		=      chars_a.find(symbol.upper()				)
																		if symbol.isupper():
																								translated += chars_b[sym_index].upper()
																		else:
																								translated += chars_b[sym_index].lower()
												else:
																		# symbol is not in LETTERS, just add it
																		translated += symbol
						return translated

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""encrypt"""				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""decrypt"""				)

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Optional[Any]     		=      """Hello World"""
						__magic_name__    :     str     		=      """QWERTYUIOPASDFGHJKLZXCVBNM"""
						__magic_name__    :     List[str]     		=      """decrypt"""  # set to 'encrypt' or 'decrypt'

						if mode == "encrypt":
												__magic_name__    :     Dict     		=      encrypt_message(_A,					_A				)
						elif mode == "decrypt":
												__magic_name__    :     Any     		=      decrypt_message(_A,					_A				)
						print(f'Using the key {key}, the {mode}ed message is: {translated}'				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()
