


'''simple docstring'''






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  a     :
	def __init__( self      ,				__magic_name__      ,				__magic_name__=13      ,				__magic_name__=7      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=99      ,				__magic_name__=32      ,				__magic_name__=2      ,				__magic_name__=4      ,				__magic_name__=37      ,				__magic_name__="gelu"      ,				__magic_name__=0.1      ,				__magic_name__=0.1      ,				__magic_name__=5_12      ,				__magic_name__=16      ,				__magic_name__=2      ,				__magic_name__=0.0_2      ,				__magic_name__=3      ,				__magic_name__=4      ,				__magic_name__=None      ,				)					->							int:
						_a			      =							parent
						_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			      =							5_12
						_a			      =							16
						_a			      =							2
						_a			      =							0.0_2
						_a			      =							3
						_a			      =							4
						_a			      =							None
	def 							__UpperCAmelCase		( self						)					->							List[str]:
						_a			      =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size						)

						_a			      =							None
						if self.use_input_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			      =							None
						_a			      =							None
						_a			      =							None
						if self.use_labels:
											_a			      =							ids_tensor([self.batch_size]      ,				self.type_sequence_label_size						)
											_a			      =							ids_tensor([self.batch_size, self.seq_length]      ,				self.num_labels						)
											_a			      =							ids_tensor([self.batch_size]      ,				self.num_choices						)

						_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      ,				initializer_range=self.initializer_range      ,				return_dict=__magic_name__      ,				)

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Any:
						_a			      =							TFRoFormerModel(config=__magic_name__						)
						_a			      =							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

						_a			      =							[input_ids, input_mask]
						_a			      =							model(__magic_name__						)

						_a			      =							model(__magic_name__						)

						self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
						_a			      =							True
						_a			      =							TFRoFormerForCausalLM(config=__magic_name__						)
						_a			      =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_a			      =							model(__magic_name__						)['logits']
						self.parent.assertListEqual(
						    list(prediction_scores.numpy().shape						)      ,				[self.batch_size, self.seq_length, self.vocab_size]						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
						_a			      =							TFRoFormerForMaskedLM(config=__magic_name__						)
						_a			      =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_a			      =							model(__magic_name__						)
						self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[str]:
						_a			      =							self.num_labels
						_a			      =							TFRoFormerForSequenceClassification(config=__magic_name__						)
						_a			      =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_a			      =							model(__magic_name__						)
						self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
						_a			      =							self.num_choices
						_a			      =							TFRoFormerForMultipleChoice(config=__magic_name__						)
						_a			      =							tf.tile(tf.expand_dims(__magic_name__      ,				1						)      ,				(1, self.num_choices, 1)						)
						_a			      =							tf.tile(tf.expand_dims(__magic_name__      ,				1						)      ,				(1, self.num_choices, 1)						)
						_a			      =							tf.tile(tf.expand_dims(__magic_name__      ,				1						)      ,				(1, self.num_choices, 1)						)
						_a			      =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						    'token_type_ids': multiple_choice_token_type_ids,
						}
						_a			      =							model(__magic_name__						)
						self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Any:
						_a			      =							self.num_labels
						_a			      =							TFRoFormerForTokenClassification(config=__magic_name__						)
						_a			      =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_a			      =							model(__magic_name__						)
						self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
						_a			      =							TFRoFormerForQuestionAnswering(config=__magic_name__						)
						_a			      =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_a			      =							model(__magic_name__						)
						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 							__UpperCAmelCase		( self						)					->							Optional[int]:
						_a			      =							self.prepare_config_and_inputs()
						(
						    (
						    _a   
						)      ,   (
						    _a   
						)      ,   (
						    _a   
						)      ,   (
						    _a   
						)      ,   (
						    _a   
						)      ,   (
						    _a   
						)      ,   (
						    _a   
						)      ,      
						)			      =							config_and_inputs
						_a			      =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict





@require_tf
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
	_lowerCAmelCase 					=	(
	    (
	        TFRoFormerModel,
	        TFRoFormerForCausalLM,
	        TFRoFormerForMaskedLM,
	        TFRoFormerForQuestionAnswering,
	        TFRoFormerForSequenceClassification,
	        TFRoFormerForTokenClassification,
	        TFRoFormerForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	_lowerCAmelCase 					=	(
	    {
	        """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 {}
	)

	_lowerCAmelCase 					=	False
	_lowerCAmelCase 					=	False
	def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
											return True

						return False
	def 							__UpperCAmelCase		( self						)					->							str:
						_a			      =							TFRoFormerModelTester(self						)
						_a			      =							ConfigTester(self      ,				config_class=__magic_name__      ,				hidden_size=37						)
	def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
						self.config_tester.run_common_tests()
	def 							__UpperCAmelCase		( self						)					->							Optional[int]:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							str:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							Optional[int]:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							Optional[int]:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							List[Any]:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__magic_name__						)
	def 							__UpperCAmelCase		( self						)					->							str:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__magic_name__						)




	@slow
	def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
						_a			      =							TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'						)
						self.assertIsNotNone(__magic_name__						)





@require_tf
class  a     (     unittest.TestCase       ):
	@slow
	def 							__UpperCAmelCase		( self						)					->							List[str]:
						_a			      =							TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'						)
						_a			      =							tf.constant([[0, 1, 2, 3, 4, 5]]						)
						_a			      =							model(__magic_name__						)[0]

						# TODO Replace vocab size
						_a			      =							5_00_00

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

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

						# TODO Replace values below with what was printed above.
						_a			      =							tf.constant(
						    [
						        [
						            [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
						            [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
						            [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
						        ]
						    ]						)
						tf.debugging.assert_near(output[:, :3, :3]      ,				__magic_name__      ,				atol=1e-4						)





@require_tf
class  a     (     unittest.TestCase       ):
	_lowerCAmelCase 					=	1E-4
	def 							__UpperCAmelCase		( self						)					->							List[Any]:
						_a			      =							tf.constant([[4, 10]]						)
						_a			      =							TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,				embedding_dim=6						)

						_a			      =							emba(input_ids.shape						)
						_a			      =							tf.constant(
						    [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]						)

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




	def 							__UpperCAmelCase		( self						)					->							int:
						_a			      =							tf.constant(
						    [
						        [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
						        [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
						        [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
						    ]						)
						_a			      =							TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12      ,				embedding_dim=5_12						)
						emba([2, 16, 5_12]						)
						_a			      =							emba.weight[:3, :5]

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





@require_tf
class  a     (     unittest.TestCase       ):
	_lowerCAmelCase 					=	1E-4
	def 							__UpperCAmelCase		( self						)					->							int:
						# 2,12,16,64
						_a			      =							tf.reshape(tf.range(2 * 12 * 16 * 64      ,				dtype=tf.floataa						)      ,				shape=(2, 12, 16, 64)						) / 1_00

						_a			      =							-tf.reshape(tf.range(2 * 12 * 16 * 64      ,				dtype=tf.floataa						)      ,				shape=(2, 12, 16, 64)						) / 1_00

						_a			      =							TFRoFormerSinusoidalPositionalEmbedding(num_positions=32      ,				embedding_dim=64						)
						_a			      =							embed_positions([2, 16, 7_68]						)[None, None, :, :]

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

						_a			      =							tf.constant(
						    [
						        [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
						        [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
						        [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
						        [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
						        [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
						        [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
						    ]						)
						_a			      =							tf.constant(
						    [
						        [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
						        [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
						        [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
						        [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
						        [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
						        [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
						    ]						)

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





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :Union[str, Any]		)   ->  List[str]:




     '''simple docstring'''



     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]






def 	_A				(lowerCAmelCase__  :dict[int, list[int]]		)   ->  list[tuple[int, int]]:




     '''simple docstring'''



     _a			      =							0
     _a			      =							len(lowerCAmelCase__		)  # No of vertices in graph
     _a			      =							[0] * n
     _a			      =							[False] * n

     def dfs(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Union[str, Any]		):
          _a			      =							True
          _a			      =							id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						id_		)
                    _a			      =							min(low[at]				,						low[to]		)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)		)
               else:
                    # This edge is a back edge and cannot be a bridge
                    _a			      =							min(low[at]				,						low[to]		)

     _a			      =							[]
     for i in range(lowerCAmelCase__		):
          if not visited[i]:
               dfs(lowerCAmelCase__				,						-1				,						lowerCAmelCase__				,						id_		)
     return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

