





'''simple docstring'''






from __future__ import annotations
def      UpperCamelCase__   (	lowerCAmelCase						):





						"""simple docstring"""


						if not list_of_ints:
												return []

						if min(lowerCAmelCase						) < 0:
												raise ValueError("""All numbers must be positive"""						)

						_lowerCAmelCase							  =							max(len(bin(lowerCAmelCase						)[2:]						) for x in list_of_ints						)
						return _msd_radix_sort(lowerCAmelCase							,			lowerCAmelCase						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





						"""simple docstring"""


						if bit_position == 0 or len(lowerCAmelCase						) in [0, 1]:
												return list_of_ints

						_lowerCAmelCase							  =							[]
						_lowerCAmelCase							  =							[]
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(lowerCAmelCase						)
												else:
																		# number has a zero at bit bit_position
																		zeros.append(lowerCAmelCase						)

    # recursively split both lists further
						_lowerCAmelCase							  =							_msd_radix_sort(lowerCAmelCase							,			bit_position - 1						)
						_lowerCAmelCase							  =							_msd_radix_sort(lowerCAmelCase							,			bit_position - 1						)

						# recombine lists
						_lowerCAmelCase							  =							zeros
						res.extend(lowerCAmelCase						)

						return res
def      UpperCamelCase__   (	lowerCAmelCase						):





						"""simple docstring"""


						_lowerCAmelCase							  =							len(lowerCAmelCase						)
						if not list_of_ints or length == 1:
												return

						if min(lowerCAmelCase						) < 0:
												raise ValueError("""All numbers must be positive"""						)

						_lowerCAmelCase							  =							max(len(bin(lowerCAmelCase						)[2:]						) for x in list_of_ints						)
						_msd_radix_sort_inplace(lowerCAmelCase							,			lowerCAmelCase							,			0							,			lowerCAmelCase						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





						"""simple docstring"""


						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						_lowerCAmelCase							  =							begin_index
						_lowerCAmelCase							  =							end_index - 1
						while i <= j:
												_lowerCAmelCase							  =							False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		_lowerCAmelCase							  =							True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		_lowerCAmelCase							  =							True

												if changed:
																		continue

												_lowerCAmelCase  ,							_lowerCAmelCase							  =							list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)
						_msd_radix_sort_inplace(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()













'''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     ):




      def __init__(    self    :				str    ,    __snake_case    :				str    ,    __snake_case    :				str=13    ,    __snake_case    :				Optional[Any]=7    ,    __snake_case    :				str=True    ,    __snake_case    :				Any=True    ,    __snake_case    :				List[str]=True    ,    __snake_case    :				Any=True    ,    __snake_case    :				List[str]=99    ,    __snake_case    :				Dict=24    ,    __snake_case    :				Any=32    ,    __snake_case    :				List[str]=5    ,    __snake_case    :				Any=4    ,    __snake_case    :				Optional[Any]=37    ,    __snake_case    :				Union[str, Any]="gelu"    ,    __snake_case    :				Dict=0.1    ,    __snake_case    :				int=0.1    ,    __snake_case    :				Dict=5_12    ,    __snake_case    :				List[Any]=16    ,    __snake_case    :				Dict=2    ,    __snake_case    :				str=0.02    ,    __snake_case    :				Any=4    ,    )       ->   Union[str, Any]:
            _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							  =							embedding_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        lowercase__					(    self    :				Optional[Any]						)       ->   Optional[Any]:
            _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							  =							None
            if self.use_token_type_ids:
                  _lowerCAmelCase							  =							ids_tensor([self.batch_size, self.seq_length]    ,    self.type_vocab_size						)

            _lowerCAmelCase							  =							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        lowercase__					(    self    :				Any						)       ->   Optional[int]:
            _lowerCAmelCase							  =							self.prepare_config_and_inputs()
            _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							config_and_inputs
            _lowerCAmelCase							  =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict




@require_flax
class 				UpperCAmelCase  (    snake_case_							,	unittest.TestCase     ):
      _lowercase:    str		  =     True

      _lowercase:    Optional[Any]		  =     (
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )




      def        lowercase__					(    self    :				Tuple						)       ->   Optional[int]:
            _lowerCAmelCase							  =							FlaxElectraModelTester(self						)




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






