
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerModel,
				)
				from transformers.models.roformer.modeling_tf_roformer import (
				    TFRoFormerSelfAttention,
				    TFRoFormerSinusoidalPositionalEmbedding,
				)





class       _snake_case					:







			def __init__(				self						,   _a						,   _a=13						,   _a=7						,   _a=True						,   _a=True						,   _a=True						,   _a=True						,   _a=99						,   _a=32						,   _a=2						,   _a=4						,   _a=37						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=16						,   _a=2						,   _a=0.02						,   _a=3						,   _a=4						,   _a=None						,   ):
				__magic_name__		:		List[str]	   =     parent
				__magic_name__		:		Union[str, Any]	   =     13
				__magic_name__		:		Tuple	   =     7
				__magic_name__		:		List[str]	   =     True
				__magic_name__		:		Optional[int]	   =     True
				__magic_name__		:		List[Any]	   =     True
				__magic_name__		:		str	   =     True
				__magic_name__		:		Dict	   =     99
				__magic_name__		:		Optional[int]	   =     32
				__magic_name__		:		List[str]	   =     2
				__magic_name__		:		Any	   =     4
				__magic_name__		:		Tuple	   =     37
				__magic_name__		:		List[Any]	   =     "gelu"
				__magic_name__		:		Tuple	   =     0.1
				__magic_name__		:		int	   =     0.1
				__magic_name__		:		List[str]	   =     512
				__magic_name__		:		Union[str, Any]	   =     16
				__magic_name__		:		Dict	   =     2
				__magic_name__		:		str	   =     0.02
				__magic_name__		:		str	   =     3
				__magic_name__		:		str	   =     4
				__magic_name__		:		str	   =     None







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		int	   =     None
				if self.use_input_mask:
					__magic_name__		:		Any	   =     random_attention_mask([self.batch_size, self.seq_length]		)

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

				__magic_name__		:		Optional[int]	   =     None
				__magic_name__		:		Tuple	   =     None
				__magic_name__		:		List[Any]	   =     None
				if self.use_labels:
					__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size]						,   self.type_sequence_label_size		)
					__magic_name__		:		int	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels		)
					__magic_name__		:		Any	   =     ids_tensor([self.batch_size]						,   self.num_choices		)

				__magic_name__		:		Dict	   =     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						,   initializer_range=self.initializer_range						,   return_dict=_a						,   )

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		int	   =     TFRoFormerModel(config=_a		)
				__magic_name__		:		Tuple	   =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

				__magic_name__		:		Dict	   =     [input_ids, input_mask]
				__magic_name__		:		int	   =     model(_a		)

				__magic_name__		:		List[Any]	   =     model(_a		)

				self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		str	   =     True
				__magic_name__		:		Union[str, Any]	   =     TFRoFormerForCausalLM(config=_a		)
				__magic_name__		:		Union[str, Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				__magic_name__		:		Union[str, Any]	   =     model(_a		)["logits"]
				self.parent.assertListEqual(
				    list(prediction_scores.numpy().shape		)						,   [self.batch_size, self.seq_length, self.vocab_size]		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Optional[Any]	   =     TFRoFormerForMaskedLM(config=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				__magic_name__		:		Union[str, Any]	   =     model(_a		)
				self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Optional[Any]	   =     self.num_labels
				__magic_name__		:		int	   =     TFRoFormerForSequenceClassification(config=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				__magic_name__		:		int	   =     model(_a		)
				self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Tuple	   =     self.num_choices
				__magic_name__		:		Dict	   =     TFRoFormerForMultipleChoice(config=_a		)
				__magic_name__		:		Optional[int]	   =     tf.tile(tf.expand_dims(_a						,   1		)						,   (1, self.num_choices, 1)		)
				__magic_name__		:		int	   =     tf.tile(tf.expand_dims(_a						,   1		)						,   (1, self.num_choices, 1)		)
				__magic_name__		:		Any	   =     tf.tile(tf.expand_dims(_a						,   1		)						,   (1, self.num_choices, 1)		)
				__magic_name__		:		Optional[int]	   =     {
				    "input_ids": multiple_choice_inputs_ids,
				    "attention_mask": multiple_choice_input_mask,
				    "token_type_ids": multiple_choice_token_type_ids,
				}
				__magic_name__		:		List[Any]	   =     model(_a		)
				self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		int	   =     self.num_labels
				__magic_name__		:		int	   =     TFRoFormerForTokenClassification(config=_a		)
				__magic_name__		:		int	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				__magic_name__		:		List[Any]	   =     model(_a		)
				self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     TFRoFormerForQuestionAnswering(config=_a		)
				__magic_name__		:		List[str]	   =     {
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				__magic_name__		:		int	   =     model(_a		)
				self.parent.assertEqual(result.start_logits.shape						,   (self.batch_size, self.seq_length)		)
				self.parent.assertEqual(result.end_logits.shape						,   (self.batch_size, self.seq_length)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     self.prepare_config_and_inputs()
				(
				    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,    (
				    __magic_name__    
				)   ,        
				)		:		int	   =     config_and_inputs
				__magic_name__		:		int	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_tf
class       _snake_case					(     lowercase_				,						lowercase_				,						unittest.TestCase				):
			UpperCamelCase__          =    (
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase__          =    (
			    {
			        '''feature-extraction''': TFRoFormerModel,
			        '''fill-mask''': TFRoFormerForMaskedLM,
			        '''question-answering''': TFRoFormerForQuestionAnswering,
			        '''text-classification''': TFRoFormerForSequenceClassification,
			        '''text-generation''': TFRoFormerForCausalLM,
			        '''token-classification''': TFRoFormerForTokenClassification,
			        '''zero-shot''': TFRoFormerForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase__          =    False
			UpperCamelCase__          =    False







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a		):
				if pipeline_test_casse_name == "TextGenerationPipelineTests":
					return True

				return False







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     TFRoFormerModelTester(self		)
				__magic_name__		:		Tuple	   =     ConfigTester(self						,   config_class=_a						,   hidden_size=37		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.config_tester.run_common_tests()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"		)
				self.assertIsNotNone(_a		)







@require_tf
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
				__magic_name__		:		List[str]	   =     tf.constant([[0, 1, 2, 3, 4, 5]]		)
				__magic_name__		:		Optional[Any]	   =     model(_a		)[0]

				# TODO Replace vocab size
				__magic_name__		:		List[str]	   =     50_000

				__magic_name__		:		List[str]	   =     [1, 6, vocab_size]
				self.assertEqual(output.shape						,   _a		)

				print(output[:, :3, :3]		)

				# TODO Replace values below with what was printed above.
				__magic_name__		:		str	   =     tf.constant(
				    [
				        [
				            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
				            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
				            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
				        ]
				    ]		)
				tf.debugging.assert_near(output[:, :3, :3]						,   _a						,   atol=1e-4		)







@require_tf
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    1e-4







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     tf.constant([[4, 10]]		)
				__magic_name__		:		Optional[Any]	   =     TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,   embedding_dim=6		)

				__magic_name__		:		Optional[int]	   =     emba(input_ids.shape		)
				__magic_name__		:		Dict	   =     tf.constant(
				    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]		)

				tf.debugging.assert_near(_a						,   _a						,   atol=self.tolerance		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     tf.constant(
				    [
				        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
				        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
				        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
				    ]		)
				__magic_name__		:		Any	   =     TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,   embedding_dim=512		)
				emba([2, 16, 512]		)
				__magic_name__		:		str	   =     emba.weight[:3, :5]

				tf.debugging.assert_near(_a						,   _a						,   atol=self.tolerance		)







@require_tf
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    1e-4







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# 2,12,16,64
				__magic_name__		:		int	   =     tf.reshape(tf.range(2 * 12 * 16 * 64						,   dtype=tf.floataa		)						,   shape=(2, 12, 16, 64)		) / 100

				__magic_name__		:		Optional[Any]	   =     -tf.reshape(tf.range(2 * 12 * 16 * 64						,   dtype=tf.floataa		)						,   shape=(2, 12, 16, 64)		) / 100

				__magic_name__		:		List[str]	   =     TFRoFormerSinusoidalPositionalEmbedding(num_positions=32						,   embedding_dim=64		)
				__magic_name__		:		Tuple	   =     embed_positions([2, 16, 768]		)[None, None, :, :]

				__magic_name__   ,    __magic_name__		:		Any	   =     TFRoFormerSelfAttention.apply_rotary_position_embeddings(
				    _a						,   _a						,   _a		)

				__magic_name__		:		List[Any]	   =     tf.constant(
				    [
				        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
				        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
				        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
				        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
				        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
				        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
				    ]		)
				__magic_name__		:		int	   =     tf.constant(
				    [
				        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
				        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
				        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
				        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
				        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
				        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
				    ]		)

				tf.debugging.assert_near(query_layer[0, 0, :6, :8]						,   _a						,   atol=self.tolerance		)
				tf.debugging.assert_near(key_layer[0, 0, :6, :8]						,   _a						,   atol=self.tolerance		)



def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_							=    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					snake_case_							=    max(
																					    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )   ,							mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							j - wt[i - 1]      ) + val[i - 1]   ,							)
														snake_case_							=    val
							return f[i][j]





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [[0] * (w + 1) for _ in range(n + 1      )]

							for i in range(1   ,							n + 1      ):
														for w_ in range(1   ,							w + 1      ):
																					if wt[i - 1] <= w_:
																												snake_case_							=    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,							dp[i - 1][w_]      )
																					else:
																												snake_case_							=    dp[i - 1][w_]

							return dp[n][w_], dp





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__   ,							(list, tuple)      ) and isinstance(UpperCamelCase__   ,							(list, tuple)      )):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'      )

							snake_case_							=    len(UpperCamelCase__      )
							if num_items != len(UpperCamelCase__      ):
														snake_case_							=    (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__      )} values'''
														)
														raise ValueError(UpperCamelCase__      )
							for i in range(UpperCamelCase__      ):
														if not isinstance(wt[i]   ,							UpperCamelCase__      ):
																					snake_case_							=    (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]      )} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__      )

							snake_case_				,    snake_case_							=    knapsack(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    set()
							_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							return optimal_val, example_optional_set





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					optimal_set.add(UpperCamelCase__      )
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							j - wt[i - 1]   ,							UpperCamelCase__      )


if __name__ == "__main__":
					_UpperCAmelCase		:   Tuple										=       [3, 2, 4, 4]
					_UpperCAmelCase		:   Optional[Any]										=       [4, 3, 2, 3]
					_UpperCAmelCase		:   List[str]										=       4
					_UpperCAmelCase		:   str										=       6
					_UpperCAmelCase		:   Tuple										=       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase				,						_UpperCAmelCase		:   List[Any]										=       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase				,						_UpperCAmelCase		:   Any										=       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




