



import unittest

import numpy as np

from transformers import RoFormerConfig, 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():
							import jax.numpy as jnp

							from transformers.models.roformer.modeling_flax_roformer import (
							    FlaxRoFormerForMaskedLM,
							    FlaxRoFormerForMultipleChoice,
							    FlaxRoFormerForQuestionAnswering,
							    FlaxRoFormerForSequenceClassification,
							    FlaxRoFormerForTokenClassification,
							    FlaxRoFormerModel,
							)


class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def __init__(  self       :							Any					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Tuple=13					,       _snake_case       :							int=7					,       _snake_case       :							Dict=True					,       _snake_case       :							str=True					,       _snake_case       :							Tuple=True					,       _snake_case       :							List[str]=True					,       _snake_case       :							Union[str, Any]=99					,       _snake_case       :							Union[str, Any]=32					,       _snake_case       :							Union[str, Any]=5					,       _snake_case       :							Any=4					,       _snake_case       :							Any=37					,       _snake_case       :							Any="gelu"					,       _snake_case       :							Optional[Any]=0.1					,       _snake_case       :							Any=0.1					,       _snake_case       :							List[str]=5_12					,       _snake_case       :							int=16					,       _snake_case       :							str=2					,       _snake_case       :							Any=0.02					,       _snake_case       :							List[str]=4					,       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


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

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

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

								A__	  =     RoFormerConfig(
								    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=_snake_case					,       initializer_range=self.initializer_range					,       )

								return config, input_ids, token_type_ids, attention_mask



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


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







@require_flax
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		str      							=     True

					A__					:		Optional[Any]      							=     (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     FlaxRoFormerModelTester(self       )



					@slow
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								for model_class_name in self.all_model_classes:
											A__	  =     model_class_name.from_pretrained('junnyu/roformer_chinese_small'					,       from_pt=_snake_case       )
											A__	  =     model(np.ones((1, 1)       )       )
											self.assertIsNotNone(_snake_case       )







@require_flax
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@slow
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'       )
								A__	  =     jnp.array([[0, 1, 2, 3, 4, 5]]       )
								A__	  =     model(_snake_case       )[0]

								A__	  =     5_00_00

								A__	  =     (1, 6, vocab_size)
								self.assertEqual(output.shape					,       _snake_case       )

								A__	  =     jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]       )

								self.assertTrue(jnp.allclose(output[:, :3, :3]					,       _snake_case					,       atol=1E-4       )       )









def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     0
			A__	  =     len(__UpperCamelCase       ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__UpperCamelCase       ):
									return None

						A__	  =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												A__	  =     left
												A__	  =     point
									elif point > right:
												A__	  =     right
												A__	  =     point
									else:
												if item < current_item:
															A__	  =     point - 1
												else:
															A__	  =     point + 1
			return None


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__UpperCamelCase       ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			elif point > right:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						point - 1       )
						else:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						point + 1			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			if collection != sorted(__UpperCamelCase       ):
						raise ValueError('Collection must be ascending sorted'       )
			return True


if __name__ == "__main__":
							import sys
							SCREAMING_SNAKE_CASE__   			=   0
							if debug == 1:
														SCREAMING_SNAKE_CASE__   			=   [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														SCREAMING_SNAKE_CASE__   			=   6_7

							SCREAMING_SNAKE_CASE__   			=   interpolation_search(collection, target)
							if result is not None:
														print(f'{target} found at positions: {result}')
							else:
														print('''Not found''')




