



import argparse
import copy






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]	)  ->						Dict:
					__lowerCAmelCase	   ={}

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

					return dict_of_neighbours






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Any	)  ->						Tuple:

					with open(__lowerCamelCase	) as f:
										__lowerCAmelCase	   =f.read(1	)
					__lowerCAmelCase	   =start_node

					__lowerCAmelCase	   =[]

					__lowerCAmelCase	   =start_node

					__lowerCAmelCase	   =0
					while visiting not in first_solution:
										__lowerCAmelCase	   =10000
										for k in dict_of_neighbours[visiting]:
															if int(k[1]	) < int(__lowerCamelCase	) and k[0] not in first_solution:
																				__lowerCAmelCase	   =k[1]
																				__lowerCAmelCase	   =k[0]

										first_solution.append(__lowerCamelCase	)
										__lowerCAmelCase	   =distance_of_first_solution + int(__lowerCamelCase	)
										__lowerCAmelCase	   =best_node

					first_solution.append(__lowerCamelCase	)

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

					__lowerCAmelCase	   =(
					    distance_of_first_solution
					    + int(dict_of_neighbours[first_solution[-2]][position][1]	)
					    - 10000
					)
					return first_solution, distance_of_first_solution






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    str	)  ->						Optional[int]:
					__lowerCAmelCase	   =[]

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

															__lowerCAmelCase	   =copy.deepcopy(__lowerCamelCase	)
															__lowerCAmelCase	   =kn
															__lowerCAmelCase	   =n

															__lowerCAmelCase	   =0

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

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

					__lowerCAmelCase	   =len(neighborhood_of_solution[0]	) - 1

					neighborhood_of_solution.sort(key=lambda __lowerCamelCase	: x[index_of_last_item_in_the_list]	)
					return neighborhood_of_solution






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =1
					__lowerCAmelCase	   =first_solution
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =distance_of_first_solution
					__lowerCAmelCase	   =solution

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

										__lowerCAmelCase	   =False
										while not found:
															__lowerCAmelCase	   =0
															while i < len(__lowerCamelCase	):
																				if best_solution[i] != solution[i]:
																									__lowerCAmelCase	   =best_solution[i]
																									__lowerCAmelCase	   =solution[i]
																									break
																				__lowerCAmelCase	   =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]	)
																				__lowerCAmelCase	   =True
																				__lowerCAmelCase	   =best_solution[:-1]
																				__lowerCAmelCase	   =neighborhood[index_of_best_solution][best_cost_index]
																				if cost < best_cost:
																									__lowerCAmelCase	   =cost
																									__lowerCAmelCase	   =solution
															else:
																				__lowerCAmelCase	   =index_of_best_solution + 1
																				__lowerCAmelCase	   =neighborhood[index_of_best_solution]

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

										__lowerCAmelCase	   =count + 1

					return best_solution_ever, best_cost






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any=None	)  ->						List[Any]:
					__lowerCAmelCase	   =generate_neighbours(args.File	)

					__lowerCAmelCase   ,	__lowerCAmelCase	   =generate_first_solution(
					    args.File				,   __lowerCamelCase	)

					__lowerCAmelCase   ,	__lowerCAmelCase	   =tabu_search(
					    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   args.Iterations				,   args.Size				,   )

					print(f"""Best solution: {best_sol}, with total distance: {best_cost}."""	)


if __name__ == "__main__":
				lowercase_	       =			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())









import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    XLMForMultipleChoice,
				    XLMForQuestionAnswering,
				    XLMForQuestionAnsweringSimple,
				    XLMForSequenceClassification,
				    XLMForTokenClassification,
				    XLMModel,
				    XLMWithLMHeadModel,
				)
				from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class 						__a :



	def __init__(						self							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    Optional[Any]=13				,	snake_case_							:    List[str]=7				,	snake_case_							:    Tuple=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    str=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    List[Any]=False				,	snake_case_							:    str=False				,	snake_case_							:    List[str]=False				,	snake_case_							:    List[Any]=2				,	snake_case_							:    List[Any]=99				,	snake_case_							:    Tuple=0				,	snake_case_							:    Optional[int]=32				,	snake_case_							:    Union[str, Any]=5				,	snake_case_							:    Any=4				,	snake_case_							:    str=0.1				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Optional[Any]=5_12				,	snake_case_							:    str=2				,	snake_case_							:    List[str]=0.0_2				,	snake_case_							:    Optional[int]=2				,	snake_case_							:    Union[str, Any]=4				,	snake_case_							:    str="last"				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    int=None				,	snake_case_							:    Any=0				,	)->							Tuple:
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =seq_length
						__lowerCAmelCase	   =is_training
						__lowerCAmelCase	   =use_input_lengths
						__lowerCAmelCase	   =use_token_type_ids
						__lowerCAmelCase	   =use_labels
						__lowerCAmelCase	   =gelu_activation
						__lowerCAmelCase	   =sinusoidal_embeddings
						__lowerCAmelCase	   =causal
						__lowerCAmelCase	   =asm
						__lowerCAmelCase	   =n_langs
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =n_special
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =type_sequence_label_size
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =num_labels
						__lowerCAmelCase	   =num_choices
						__lowerCAmelCase	   =summary_type
						__lowerCAmelCase	   =use_proj
						__lowerCAmelCase	   =scope
						__lowerCAmelCase	   =bos_token_id



	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)
						__lowerCAmelCase	   =random_attention_mask([self.batch_size, self.seq_length])

						__lowerCAmelCase	   =None
						if self.use_input_lengths:
											__lowerCAmelCase	   =(
											    ids_tensor([self.batch_size]				,	vocab_size=2) + self.seq_length - 2
											)  # small variation of seq_length

						__lowerCAmelCase	   =None
						if self.use_token_type_ids:
											__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.n_langs)

						__lowerCAmelCase	   =None
						__lowerCAmelCase	   =None
						__lowerCAmelCase	   =None
						if self.use_labels:
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	self.type_sequence_label_size)
											__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels)
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	2).float()
											__lowerCAmelCase	   =ids_tensor([self.batch_size]				,	self.num_choices)

						__lowerCAmelCase	   =self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)



	def 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						return XLMConfig(
						    vocab_size=self.vocab_size				,	n_special=self.n_special				,	emb_dim=self.hidden_size				,	n_layers=self.num_hidden_layers				,	n_heads=self.num_attention_heads				,	dropout=self.hidden_dropout_prob				,	attention_dropout=self.attention_probs_dropout_prob				,	gelu_activation=self.gelu_activation				,	sinusoidal_embeddings=self.sinusoidal_embeddings				,	asm=self.asm				,	causal=self.causal				,	n_langs=self.n_langs				,	max_position_embeddings=self.max_position_embeddings				,	initializer_range=self.initializer_range				,	summary_type=self.summary_type				,	use_proj=self.use_proj				,	num_labels=self.num_labels				,	bos_token_id=self.bos_token_id				,	)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	)->							Tuple:
						__lowerCAmelCase	   =XLMModel(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	lengths=snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    Dict				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    int				,	)->							Dict:
						__lowerCAmelCase	   =XLMWithLMHeadModel(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.loss.shape				,	())
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    int				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Dict:
						__lowerCAmelCase	   =XLMForQuestionAnsweringSimple(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)
						__lowerCAmelCase	   =outputs
						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							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    int				,	snake_case_							:    int				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Optional[int]:
						__lowerCAmelCase	   =XLMForQuestionAnswering(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	p_mask=snake_case_				,	)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	)

						((__lowerCAmelCase)   ,	)	   =result_with_labels.to_tuple()

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)

						((__lowerCAmelCase)   ,	)	   =result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape				,	())
						self.parent.assertEqual(result.start_top_log_probs.shape				,	(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape				,	(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape				,	(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape				,	(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape				,	(self.batch_size,))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    Optional[int]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Optional[Any]				,	)->							int:
						__lowerCAmelCase	   =XLMForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.loss.shape				,	())
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size))



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    int				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	)->							Optional[Any]:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =XLMForTokenClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels))



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    Tuple				,	snake_case_							:    List[str]				,	snake_case_							:    Tuple				,	)->							List[Any]:
						__lowerCAmelCase	   =self.num_choices
						__lowerCAmelCase	   =XLMForMultipleChoice(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =input_ids.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =token_type_ids.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =input_mask.unsqueeze(1).expand(-1				,	self.num_choices				,	-1).contiguous()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices))



	def 		UpperCamelCase							(						self							:    Optional[Any])->							List[Any]:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	
						)	   =config_and_inputs
						__lowerCAmelCase	   ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
						return config, inputs_dict






@require_torch
class 						__a (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE       =       (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	SCREAMING_SNAKE_CASE       =       (
	    {
	        "feature-extraction": XLMModel,
	        "fill-mask": XLMWithLMHeadModel,
	        "question-answering": XLMForQuestionAnsweringSimple,
	        "text-classification": XLMForSequenceClassification,
	        "text-generation": XLMWithLMHeadModel,
	        "token-classification": XLMForTokenClassification,
	        "zero-shot": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    str)->							Tuple:
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("""Fast""")
						):
											# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
											# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
											# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
											return True

						return False



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Tuple				,	snake_case_							:    Any				,	snake_case_							:    str=False)->							str:
						__lowerCAmelCase	   =super()._prepare_for_class(snake_case_				,	snake_case_				,	return_labels=snake_case_)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)

						return inputs_dict



	def 		UpperCamelCase							(						self							:    int)->							List[Any]:
						__lowerCAmelCase	   =XLMModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	emb_dim=37)



	def 		UpperCamelCase							(						self							:    List[str])->							Any:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase							(						self							:    Optional[int])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    int				,	snake_case_							:    Optional[int]=False				,	snake_case_							:    List[Any]=1)->							Optional[Any]:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_attentions in attentions]				,	[True] * len(snake_case_))
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_attentions in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =min_length + idx + 1

											__lowerCAmelCase	   =(
											    batch_size * num_beam_groups,
											    config.num_attention_heads,
											    tgt_len,
											    src_len,
											)
											# check attn size
											self.assertListEqual(
											    [layer_attention.shape for layer_attention in iter_attentions]				,	[expected_shape] * len(snake_case_))



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[str]=False				,	snake_case_							:    Any=1)->							Any:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_hidden_states in hidden_states]				,	[True] * len(snake_case_)				,	)
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_hidden_states in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =(batch_size * num_beam_groups, seq_len, config.hidden_size)
											# check hidden size
											self.assertListEqual(
											    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,	[expected_shape] * len(snake_case_)				,	)
						pass



	@slow
	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase	   =XLMModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)






@require_torch
class 						__a (     unittest.TestCase				):



	@slow
	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						__lowerCAmelCase	   =XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""")
						model.to(snake_case_)
						__lowerCAmelCase	   =torch.tensor([[14, 4_47]]				,	dtype=torch.long				,	device=snake_case_)  # the president
						__lowerCAmelCase	   =[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						__lowerCAmelCase	   =model.generate(snake_case_				,	do_sample=snake_case_)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,	snake_case_)




