





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def __init__(self       :      List[Any]				,			a__       :      Optional[int]				,			a__       :      Optional[int]=13				,			a__       :      List[Any]=7				,			a__       :      Dict=True				,			a__       :      Optional[Any]=True				,			a__       :      List[Any]=True				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=99				,			a__       :      Optional[Any]=32				,			a__       :      List[str]=5				,			a__       :      Any=4				,			a__       :      str=37				,			a__       :      Optional[int]="gelu"				,			a__       :      Optional[Any]=0.1				,			a__       :      Dict=0.1				,			a__       :      Any=512				,			a__       :      Union[str, Any]=16				,			a__       :      Any=2				,			a__       :      Optional[int]=0.0_2				,			a__       :      Optional[int]=4				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __snake_case							       =   is_training
         __snake_case							       =   use_attention_mask
         __snake_case							       =   use_token_type_ids
         __snake_case							       =   use_labels
         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   type_sequence_label_size
         __snake_case							       =   initializer_range
         __snake_case							       =   num_choices


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size      )

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

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

         __snake_case							       =   RobertaPreLayerNormConfig(
             vocab_size=self.vocab_size				,			hidden_size=self.hidden_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				,			is_decoder=a__				,			initializer_range=self.initializer_range				,			)

         return config, input_ids, token_type_ids, attention_mask


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   config_and_inputs
         __snake_case							       =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   config_and_inputs

         __snake_case							       =   True
         __snake_case							       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2      )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Any							=    True

      A_						:				Optional[Any]							=    (
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   FlaxRobertaPreLayerNormModelTester(self      )


      @slow
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         for model_class_name in self.all_model_classes:
            __snake_case							       =   model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,			from_pt=a__      )
            __snake_case							       =   model(np.ones((1, 1)      )      )
            self.assertIsNotNone(a__      )






@require_flax
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @slow
      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,			from_pt=a__      )

         __snake_case							       =   np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				,			dtype=jnp.intaa      )
         __snake_case							       =   model(a__      )[0]
         __snake_case							       =   [1, 11, 5_0265]
         self.assertEqual(list(output.shape      )				,			a__      )
         # compare the actual values for a slice.
         __snake_case							       =   np.array(
             [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,			dtype=np.floataa      )
         self.assertTrue(np.allclose(output[:, :3, :3]				,			a__				,			atol=1E-4      )      )


      @slow
      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,			from_pt=a__      )

         __snake_case							       =   np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				,			dtype=jnp.intaa      )
         __snake_case							       =   model(a__      )[0]
         # compare the actual values for a slice.
         __snake_case							       =   np.array(
             [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,			dtype=np.floataa      )
         self.assertTrue(np.allclose(output[:, :3, :3]				,			a__				,			atol=1E-4      )      )






from __future__ import annotations

import bisect






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  )

							while lo < hi:
														_lowerCAmelCase     :							Optional[Any]    =    lo + (hi - lo) // 2
														if sorted_collection[mid] < item:
																					_lowerCAmelCase     :							Union[str, Any]    =    mid + 1
														else:
																					_lowerCAmelCase     :							str    =    mid

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							str    =    len(_lowerCamelCase  )

							while lo < hi:
														_lowerCAmelCase     :							Tuple    =    lo + (hi - lo) // 2
														if sorted_collection[mid] <= item:
																					_lowerCAmelCase     :							Dict    =    mid + 1
														else:
																					_lowerCAmelCase     :							str    =    mid

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_left(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_right(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    0
							_lowerCAmelCase     :							Union[str, Any]    =    len(_lowerCamelCase  ) - 1

							while left <= right:
														_lowerCAmelCase     :							int    =    left + (right - left) // 2
														_lowerCAmelCase     :							int    =    sorted_collection[midpoint]
														if current_item == item:
																					return midpoint
														elif item < current_item:
																					_lowerCAmelCase     :							str    =    midpoint - 1
														else:
																					_lowerCAmelCase     :							Any    =    midpoint + 1
							return None






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    bisect.bisect_left(_lowerCamelCase				, _lowerCamelCase  )
							if index != len(_lowerCamelCase  ) and sorted_collection[index] == item:
														return index
							return None






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if right < left:
														return None

							_lowerCAmelCase     :							Optional[int]    =    left + (right - left) // 2

							if sorted_collection[midpoint] == item:
														return midpoint
							elif sorted_collection[midpoint] > item:
														return binary_search_by_recursion(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, midpoint - 1  )
							else:
														return binary_search_by_recursion(_lowerCamelCase				, _lowerCamelCase				, midpoint + 1				, _lowerCamelCase  )


if __name__ == "__main__":
						_snake_case		=  input("Enter numbers separated by comma:\n").strip()
						_snake_case		=  sorted(int(item) for item in user_input.split(","))
						_snake_case		=  int(input("Enter a single number to be found in the list:\n"))
						_snake_case		=  binary_search(collection, target)
						if result is None:
												print(f'''{target} was not found in {collection}.''')
						else:
												print(f'''{target} was found at position {result} in {collection}.''')
