


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__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=3_2      , __snake_case=2      , __snake_case=4      , __snake_case=3_7      , __snake_case="gelu"      , __snake_case=0.1      , __snake_case=0.1      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=None      , ):
         snake_case					=		parent
         snake_case					=		1_3
         snake_case					=		7
         snake_case					=		True
         snake_case					=		True
         snake_case					=		True
         snake_case					=		True
         snake_case					=		9_9
         snake_case					=		3_2
         snake_case					=		2
         snake_case					=		4
         snake_case					=		3_7
         snake_case					=		'''gelu'''
         snake_case					=		0.1
         snake_case					=		0.1
         snake_case					=		5_1_2
         snake_case					=		1_6
         snake_case					=		2
         snake_case					=		0.02
         snake_case					=		3
         snake_case					=		4
         snake_case					=		None
    def 		a_      (	self   ):
         snake_case					=		ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

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

         snake_case					=		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=__snake_case      , )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		TFRoFormerModel(config=__snake_case   )
         snake_case					=		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         snake_case					=		[input_ids, input_mask]
         snake_case					=		model(__snake_case   )

         snake_case					=		model(__snake_case   )

         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		True
         snake_case					=		TFRoFormerForCausalLM(config=__snake_case   )
         snake_case					=		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         snake_case					=		model(__snake_case   )['''logits''']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape   )      , [self.batch_size, self.seq_length, self.vocab_size]   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		TFRoFormerForMaskedLM(config=__snake_case   )
         snake_case					=		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         snake_case					=		model(__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		self.num_labels
         snake_case					=		TFRoFormerForSequenceClassification(config=__snake_case   )
         snake_case					=		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         snake_case					=		model(__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		self.num_choices
         snake_case					=		TFRoFormerForMultipleChoice(config=__snake_case   )
         snake_case					=		tf.tile(tf.expand_dims(__snake_case      , 1   )      , (1, self.num_choices, 1)   )
         snake_case					=		tf.tile(tf.expand_dims(__snake_case      , 1   )      , (1, self.num_choices, 1)   )
         snake_case					=		tf.tile(tf.expand_dims(__snake_case      , 1   )      , (1, self.num_choices, 1)   )
         snake_case					=		{
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         snake_case					=		model(__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_choices)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		self.num_labels
         snake_case					=		TFRoFormerForTokenClassification(config=__snake_case   )
         snake_case					=		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         snake_case					=		model(__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=		TFRoFormerForQuestionAnswering(config=__snake_case   )
         snake_case					=		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         snake_case					=		model(__snake_case   )
         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 		a_      (	self   ):
         snake_case					=		self.prepare_config_and_inputs()
         (
             (
             snake_case  
         )			,  (
             snake_case  
         )			,  (
             snake_case  
         )			,  (
             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''': input_mask}
         return config, inputs_dict






@require_tf
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __magic_name__        =		(
        {
            '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 {}
    )

    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              return True

         return False
    def 		a_      (	self   ):
         snake_case					=		TFRoFormerModelTester(self   )
         snake_case					=		ConfigTester(self      , config_class=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__snake_case   )




    @slow
    def 		a_      (	self   ):
         snake_case					=		TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''   )
         self.assertIsNotNone(__snake_case   )






@require_tf
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @slow
    def 		a_      (	self   ):
         snake_case					=		TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''   )
         snake_case					=		tf.constant([[0, 1, 2, 3, 4, 5]]   )
         snake_case					=		model(__snake_case   )[0]

         # TODO Replace vocab size
         snake_case					=		5_0_0_0_0

         snake_case					=		[1, 6, vocab_size]
         self.assertEqual(output.shape      , __snake_case   )

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

         # TODO Replace values below with what was printed above.
         snake_case					=		tf.constant(
             [
                 [
                     [-0.1205_3341, -1.026_4901, 0.2922_1946],
                     [-1.513_3783, 0.19_7433, 0.1519_0607],
                     [-5.013_5403, -3.90_0256, -0.8403_8764],
                 ]
             ]   )
         tf.debugging.assert_near(output[:, :3, :3]      , __snake_case      , atol=1E-4   )






@require_tf
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		1e-4
    def 		a_      (	self   ):
         snake_case					=		tf.constant([[4, 1_0]]   )
         snake_case					=		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      , embedding_dim=6   )

         snake_case					=		emba(input_ids.shape   )
         snake_case					=		tf.constant(
             [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]   )

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




    def 		a_      (	self   ):
         snake_case					=		tf.constant(
             [
                 [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                 [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                 [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
             ]   )
         snake_case					=		TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2      , embedding_dim=5_1_2   )
         emba([2, 1_6, 5_1_2]   )
         snake_case					=		emba.weight[:3, :5]

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






@require_tf
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		1e-4
    def 		a_      (	self   ):
         # 2,12,16,64
         snake_case					=		tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      , dtype=tf.floataa   )      , shape=(2, 1_2, 1_6, 6_4)   ) / 1_0_0

         snake_case					=		-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      , dtype=tf.floataa   )      , shape=(2, 1_2, 1_6, 6_4)   ) / 1_0_0

         snake_case					=		TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2      , embedding_dim=6_4   )
         snake_case					=		embed_positions([2, 1_6, 7_6_8]   )[None, None, :, :]

         snake_case			,  snake_case					=		TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             __snake_case      , __snake_case      , __snake_case   )

         snake_case					=		tf.constant(
             [
                 [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                 [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                 [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                 [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                 [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                 [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
             ]   )
         snake_case					=		tf.constant(
             [
                 [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                 [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                 [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                 [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                 [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                 [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
             ]   )

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








import argparse
import copy
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    {}

     with open(UpperCamelCase_    ) as f:
          for line in f:
               if line.split()[0] not in dict_of_neighbours:
                    snake_case					=    []
                    _list.append([line.split()[1], line.split()[2]]    )
                    snake_case					=    _list
               else:
                    dict_of_neighbours[line.split()[0]].append(
                        [line.split()[1], line.split()[2]]    )
               if line.split()[1] not in dict_of_neighbours:
                    snake_case					=    []
                    _list.append([line.split()[0], line.split()[2]]    )
                    snake_case					=    _list
               else:
                    dict_of_neighbours[line.split()[1]].append(
                        [line.split()[0], line.split()[2]]    )

     return dict_of_neighbours
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     with open(UpperCamelCase_    ) as f:
          snake_case					=    f.read(1    )
     snake_case					=    start_node

     snake_case					=    []

     snake_case					=    start_node

     snake_case					=    0
     while visiting not in first_solution:
          snake_case					=    1_00_00
          for k in dict_of_neighbours[visiting]:
               if int(k[1]    ) < int(UpperCamelCase_    ) and k[0] not in first_solution:
                    snake_case					=    k[1]
                    snake_case					=    k[0]

          first_solution.append(UpperCamelCase_    )
          snake_case					=    distance_of_first_solution + int(UpperCamelCase_    )
          snake_case					=    best_node

     first_solution.append(UpperCamelCase_    )

     snake_case					=    0
     for k in dict_of_neighbours[first_solution[-2]]:
          if k[0] == start_node:
               break
          position += 1

     snake_case					=    (
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]    )
         - 1_00_00
     )
     return first_solution, distance_of_first_solution
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []

     for n in solution[1:-1]:
          snake_case					=    solution.index(UpperCamelCase_    )
          for kn in solution[1:-1]:
               snake_case					=    solution.index(UpperCamelCase_    )
               if n == kn:
                    continue

               snake_case					=    copy.deepcopy(UpperCamelCase_    )
               snake_case					=    kn
               snake_case					=    n

               snake_case					=    0

               for k in _tmp[:-1]:
                    snake_case					=    _tmp[_tmp.index(UpperCamelCase_    ) + 1]
                    for i in dict_of_neighbours[k]:
                         if i[0] == next_node:
                              snake_case					=    distance + int(i[1]    )
               _tmp.append(UpperCamelCase_    )

               if _tmp not in neighborhood_of_solution:
                    neighborhood_of_solution.append(_tmp    )

     snake_case					=    len(neighborhood_of_solution[0]    ) - 1

     neighborhood_of_solution.sort(key=lambda UpperCamelCase_    : x[index_of_last_item_in_the_list]    )
     return neighborhood_of_solution
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    1
     snake_case					=    first_solution
     snake_case					=    []
     snake_case					=    distance_of_first_solution
     snake_case					=    solution

     while count <= iters:
          snake_case					=    find_neighborhood(UpperCamelCase_							,UpperCamelCase_    )
          snake_case					=    0
          snake_case					=    neighborhood[index_of_best_solution]
          snake_case					=    len(UpperCamelCase_    ) - 1

          snake_case					=    False
          while not found:
               snake_case					=    0
               while i < len(UpperCamelCase_    ):
                    if best_solution[i] != solution[i]:
                         snake_case					=    best_solution[i]
                         snake_case					=    solution[i]
                         break
                    snake_case					=    i + 1

               if [first_exchange_node, second_exchange_node] not in tabu_list and [
                   second_exchange_node,
                   first_exchange_node,
               ] not in tabu_list:
                    tabu_list.append([first_exchange_node, second_exchange_node]    )
                    snake_case					=    True
                    snake_case					=    best_solution[:-1]
                    snake_case					=    neighborhood[index_of_best_solution][best_cost_index]
                    if cost < best_cost:
                         snake_case					=    cost
                         snake_case					=    solution
               else:
                    snake_case					=    index_of_best_solution + 1
                    snake_case					=    neighborhood[index_of_best_solution]

          if len(UpperCamelCase_    ) >= size:
               tabu_list.pop(0    )

          snake_case					=    count + 1

     return best_solution_ever, best_cost
def       UpperCAmelCase__						(UpperCamelCase_=None    ):



     """simple docstring"""






     snake_case					=    generate_neighbours(args.File    )

     snake_case			,  snake_case					=    generate_first_solution(
         args.File							,UpperCamelCase_    )

     snake_case			,  snake_case					=    tabu_search(
         UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,args.Iterations							,args.Size							,)

     print(F'''Best solution: {best_sol}, with total distance: {best_cost}.'''    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   argparse.ArgumentParser(description="Tabu Search")
 parser.add_argument(
     "-f",
     "--File",
     type=str,
     help="Path to the file containing the data",
     required=True,
 )
 parser.add_argument(
     "-i",
     "--Iterations",
     type=int,
     help="How many iterations the algorithm should perform",
     required=True,
 )
 parser.add_argument(
     "-s", "--Size", type=int, help="Size of the tabu list", required=True
 )

 # Pass the arguments to main method
 main(parser.parse_args())




