


snake_case_  :	List[Any]							  =							[0.33, 0.44, 0.55, 0.44, 0.33]
snake_case_  :	Optional[int]							  =							3
snake_case_  :	Dict							  =							5

# Buffer Space (with Parameters Space)
snake_case_  :	list[float]							  =							[]
snake_case_  :	list[float]							  =							[]

# Machine Time
snake_case_  :	Optional[Any]							  =							0







def   lowerCamelCase(      a__):
				global buffer_space, params_space, machine_time, K, m, t

				# Choosing Dynamical Systems (All)
				for key, value in enumerate(a__):
								# Evolution Parameter
								_SCREAMING_SNAKE_CASE	     =float(seed / value)

								# Control Theory: Orbit Change
								_SCREAMING_SNAKE_CASE	     =(buffer_space[(key + 1) % m] + e) % 1

								# Control Theory: Trajectory Change
								_SCREAMING_SNAKE_CASE	     =(params_space[key] + e) % 1 + 3

								# Modification (Transition Function) - Jumps
								_SCREAMING_SNAKE_CASE	     =round(float(r * value * (1 - value))		,10)
								_SCREAMING_SNAKE_CASE	     =r  # Saving to Parameters Space

				# Logistic Map
				assert max(a__) < 1
				assert max(a__) < 4

				# Machine Time
				machine_time += 1







def   lowerCamelCase(      ):
				global buffer_space, params_space, machine_time, K, m, t

				# PRNG (Xorshift by George Marsaglia)
				def xorshift(a__		,a__):
								x ^= y >> 13
								y ^= x << 17
								x ^= y >> 5
								return x

				# Choosing Dynamical Systems (Increment)
				_SCREAMING_SNAKE_CASE	     =machine_time % m

				# Evolution (Time Length)
				for _ in range(0		,a__):
								# Variables (Position + Parameters)
								_SCREAMING_SNAKE_CASE	     =params_space[key]
								_SCREAMING_SNAKE_CASE	     =buffer_space[key]

								# Modification (Transition Function) - Flow
								_SCREAMING_SNAKE_CASE	     =round(float(r * value * (1 - value))		,10)
								_SCREAMING_SNAKE_CASE	     =(machine_time * 0.01 + r * 1.01) % 1 + 3

				# Choosing Chaotic Data
				_SCREAMING_SNAKE_CASE	     =int(buffer_space[(key + 2) % m] * (10**10))
				_SCREAMING_SNAKE_CASE	     =int(buffer_space[(key - 2) % m] * (10**10))

				# Machine Time
				machine_time += 1

				return xorshift(a__		,a__) % 0xffff_ffff







def   lowerCamelCase(      ):
				global buffer_space, params_space, machine_time, K, m, t

				_SCREAMING_SNAKE_CASE	     =K
				_SCREAMING_SNAKE_CASE	     =[0] * m
				_SCREAMING_SNAKE_CASE	     =0


if __name__ == "__main__":
							# Initialization
							reset()

							# Pushing Data (Input)
							import random

							snake_case_  :	Tuple							  =							random.sample(range(0xFF_FFF_FFF), 1_00)
							for chunk in message:
														push(chunk)

							# for controlling
							snake_case_  :	List[str]							  =							''''''

							# Pulling Data (Output)
							while inp in ("e", "E"):
														print(f"""{format(pull(), "#04x")}""")
														print(buffer_space)
														print(params_space)
														snake_case_  :	Any							  =							input('''(e)exit? ''').strip()






"""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		:    str, lowerCAmelCase__		:    Tuple, lowerCAmelCase__		:    List[str]=1_3, lowerCAmelCase__		:    Optional[int]=7, lowerCAmelCase__		:    Optional[Any]=True, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    Any=True, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Any=9_9, lowerCAmelCase__		:    Union[str, Any]=2_4, lowerCAmelCase__		:    Optional[Any]=3_2, lowerCAmelCase__		:    Tuple=5, lowerCAmelCase__		:    int=4, lowerCAmelCase__		:    List[Any]=3_7, lowerCAmelCase__		:    Dict="gelu", lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    str=5_1_2, lowerCAmelCase__		:    Tuple=1_6, lowerCAmelCase__		:    int=2, lowerCAmelCase__		:    int=0.02, lowerCAmelCase__		:    Optional[int]=4, )	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					str     =    seq_length
										_UpperCamelCase			:					Any     =    is_training
										_UpperCamelCase			:					Union[str, Any]     =    use_attention_mask
										_UpperCamelCase			:					List[Any]     =    use_token_type_ids
										_UpperCamelCase			:					str     =    use_labels
										_UpperCamelCase			:					Union[str, Any]     =    vocab_size
										_UpperCamelCase			:					int     =    hidden_size
										_UpperCamelCase			:					Optional[int]     =    embedding_size
										_UpperCamelCase			:					int     =    num_hidden_layers
										_UpperCamelCase			:					int     =    num_attention_heads
										_UpperCamelCase			:					Tuple     =    intermediate_size
										_UpperCamelCase			:					Optional[int]     =    hidden_act
										_UpperCamelCase			:					Any     =    hidden_dropout_prob
										_UpperCamelCase			:					Union[str, Any]     =    attention_probs_dropout_prob
										_UpperCamelCase			:					Any     =    max_position_embeddings
										_UpperCamelCase			:					List[str]     =    type_vocab_size
										_UpperCamelCase			:					Union[str, Any]     =    type_sequence_label_size
										_UpperCamelCase			:					str     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    num_choices






				def 				snake_case	( self		:    Optional[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    ids_tensor([self.batch_size, self.seq_length], self.vocab_size							)

										_UpperCamelCase			:					Dict     =    None
										if self.use_attention_mask:
																_UpperCamelCase			:					Union[str, Any]     =    random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase			:					Optional[Any]     =    None
										if self.use_token_type_ids:
																_UpperCamelCase			:					Union[str, Any]     =    ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size							)

										_UpperCamelCase			:					Optional[Any]     =    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		:    Any							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.prepare_config_and_inputs()
										_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    config_and_inputs
										_UpperCamelCase			:					List[Any]     =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




@require_flax
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    True

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






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    FlaxElectraModelTester(self							)






				@slow
				def 				snake_case	( self		:    Optional[Any]							)	->      str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						_UpperCamelCase			:					int     =    model_class_name.from_pretrained('''google/electra-small-generator'''							)
																else:
																						_UpperCamelCase			:					Any     =    model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
																_UpperCamelCase			:					List[str]     =    model(np.ones((1, 1)							)							)
																self.assertIsNotNone(lowerCAmelCase__							)



