






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
           FlaubertForMultipleChoice,
           FlaubertForQuestionAnswering,
           FlaubertForQuestionAnsweringSimple,
           FlaubertForSequenceClassification,
           FlaubertForTokenClassification,
           FlaubertModel,
           FlaubertWithLMHeadModel,
       )
       from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	str=13   ,				lowerCamelCase__						:	List[Any]=7   ,				lowerCamelCase__						:	Tuple=True   ,				lowerCamelCase__						:	Any=True   ,				lowerCamelCase__						:	str=True   ,				lowerCamelCase__						:	Tuple=True   ,				lowerCamelCase__						:	Union[str, Any]=True   ,				lowerCamelCase__						:	Union[str, Any]=False   ,				lowerCamelCase__						:	Optional[Any]=False   ,				lowerCamelCase__						:	int=False   ,				lowerCamelCase__						:	str=2   ,				lowerCamelCase__						:	Dict=99   ,				lowerCamelCase__						:	Dict=0   ,				lowerCamelCase__						:	Dict=32   ,				lowerCamelCase__						:	str=5   ,				lowerCamelCase__						:	Optional[Any]=4   ,				lowerCamelCase__						:	Optional[int]=0.1   ,				lowerCamelCase__						:	int=0.1   ,				lowerCamelCase__						:	Any=5_12   ,				lowerCamelCase__						:	int=12   ,				lowerCamelCase__						:	Optional[int]=2   ,				lowerCamelCase__						:	Tuple=0.02   ,				lowerCamelCase__						:	Optional[Any]=3   ,				lowerCamelCase__						:	Any=4   ,				lowerCamelCase__						:	List[str]="last"   ,				lowerCamelCase__						:	Tuple=None   ,				lowerCamelCase__						:	str=None   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					parent
     __UpperCamelCase	:			Optional[Any]					=					batch_size
     __UpperCamelCase	:			Optional[Any]					=					seq_length
     __UpperCamelCase	:			List[str]					=					is_training
     __UpperCamelCase	:			List[Any]					=					use_input_lengths
     __UpperCamelCase	:			int					=					use_token_type_ids
     __UpperCamelCase	:			List[str]					=					use_labels
     __UpperCamelCase	:			str					=					gelu_activation
     __UpperCamelCase	:			Tuple					=					sinusoidal_embeddings
     __UpperCamelCase	:			Tuple					=					causal
     __UpperCamelCase	:			Optional[Any]					=					asm
     __UpperCamelCase	:			str					=					n_langs
     __UpperCamelCase	:			List[Any]					=					vocab_size
     __UpperCamelCase	:			Tuple					=					n_special
     __UpperCamelCase	:			Optional[int]					=					hidden_size
     __UpperCamelCase	:			Any					=					num_hidden_layers
     __UpperCamelCase	:			str					=					num_attention_heads
     __UpperCamelCase	:			Union[str, Any]					=					hidden_dropout_prob
     __UpperCamelCase	:			Union[str, Any]					=					attention_probs_dropout_prob
     __UpperCamelCase	:			Tuple					=					max_position_embeddings
     __UpperCamelCase	:			List[Any]					=					type_vocab_size
     __UpperCamelCase	:			Tuple					=					type_sequence_label_size
     __UpperCamelCase	:			Optional[Any]					=					initializer_range
     __UpperCamelCase	:			List[str]					=					num_labels
     __UpperCamelCase	:			Any					=					num_choices
     __UpperCamelCase	:			Optional[Any]					=					summary_type
     __UpperCamelCase	:			int					=					use_proj
     __UpperCamelCase	:			Union[str, Any]					=					scope

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size			)
     __UpperCamelCase	:			str					=					random_attention_mask([self.batch_size, self.seq_length]			)

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

     __UpperCamelCase	:			Optional[int]					=					None
     if self.use_token_type_ids:
        __UpperCamelCase	:			Tuple					=					ids_tensor([self.batch_size, self.seq_length]   ,				self.n_langs			)

     __UpperCamelCase	:			List[str]					=					None
     __UpperCamelCase	:			Dict					=					None
     __UpperCamelCase	:			Dict					=					None
     if self.use_labels:
        __UpperCamelCase	:			Union[str, Any]					=					ids_tensor([self.batch_size]   ,				self.type_sequence_label_size			)
        __UpperCamelCase	:			Tuple					=					ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels			)
        __UpperCamelCase	:			Tuple					=					ids_tensor([self.batch_size]   ,				2			).float()
        __UpperCamelCase	:			Union[str, Any]					=					ids_tensor([self.batch_size]   ,				self.num_choices			)

     __UpperCamelCase	:			str					=					self.get_config()

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

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return FlaubertConfig(
         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   ,				)

  def    a					(    self						:	Optional[Any]   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Union[str, Any]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					FlaubertModel(config=lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Union[str, Any]					=					model(lowerCamelCase__   ,				lengths=lowerCamelCase__   ,				langs=lowerCamelCase__			)
     __UpperCamelCase	:			int					=					model(lowerCamelCase__   ,				langs=lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					model(lowerCamelCase__			)
     self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)			)

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	str   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					FlaubertWithLMHeadModel(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

     __UpperCamelCase	:			Dict					=					model(lowerCamelCase__   ,				token_type_ids=lowerCamelCase__   ,				labels=lowerCamelCase__			)
     self.parent.assertEqual(result.loss.shape   ,				()			)
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)			)

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any   ,				):
     """simple docstring"""

     __UpperCamelCase	:			int					=					FlaubertForQuestionAnsweringSimple(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

     __UpperCamelCase	:			int					=					model(lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					model(lowerCamelCase__   ,				start_positions=lowerCamelCase__   ,				end_positions=lowerCamelCase__			)
     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						:	int   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					FlaubertForQuestionAnswering(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

     __UpperCamelCase	:			Optional[Any]					=					model(lowerCamelCase__			)

     __UpperCamelCase	:			Optional[int]					=					model(
         lowerCamelCase__   ,				start_positions=lowerCamelCase__   ,				end_positions=lowerCamelCase__   ,				cls_index=lowerCamelCase__   ,				is_impossible=lowerCamelCase__   ,				p_mask=lowerCamelCase__   ,				)

     __UpperCamelCase	:			List[Any]					=					model(
         lowerCamelCase__   ,				start_positions=lowerCamelCase__   ,				end_positions=lowerCamelCase__   ,				cls_index=lowerCamelCase__   ,				is_impossible=lowerCamelCase__   ,				)

     ((__UpperCamelCase)	,     )	:			Any					=					result_with_labels.to_tuple()

     __UpperCamelCase	:			List[Any]					=					model(lowerCamelCase__   ,				start_positions=lowerCamelCase__   ,				end_positions=lowerCamelCase__			)

     ((__UpperCamelCase)	,     )	:			List[str]					=					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    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	str   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					FlaubertForSequenceClassification(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

     __UpperCamelCase	:			Any					=					model(lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					model(lowerCamelCase__   ,				labels=lowerCamelCase__			)

     self.parent.assertEqual(result.loss.shape   ,				()			)
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)			)

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Optional[Any]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					self.num_labels
     __UpperCamelCase	:			str					=					FlaubertForTokenClassification(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

     __UpperCamelCase	:			List[str]					=					model(lowerCamelCase__   ,				attention_mask=lowerCamelCase__   ,				labels=lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)			)

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	List[Any]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					self.num_choices
     __UpperCamelCase	:			Tuple					=					FlaubertForMultipleChoice(config=lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Optional[Any]					=					input_ids.unsqueeze(1			).expand(-1   ,				self.num_choices   ,				-1			).contiguous()
     __UpperCamelCase	:			Tuple					=					token_type_ids.unsqueeze(1			).expand(-1   ,				self.num_choices   ,				-1			).contiguous()
     __UpperCamelCase	:			Union[str, Any]					=					input_mask.unsqueeze(1			).expand(-1   ,				self.num_choices   ,				-1			).contiguous()
     __UpperCamelCase	:			int					=					model(
         lowerCamelCase__   ,				attention_mask=lowerCamelCase__   ,				token_type_ids=lowerCamelCase__   ,				labels=lowerCamelCase__   ,				)
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					self.prepare_config_and_inputs()
     (
         (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     (
         __UpperCamelCase
     )	,     
     )	:			Optional[int]					=					config_and_inputs
     __UpperCamelCase	:			Optional[int]					=					{
         """input_ids""": input_ids,
         """token_type_ids""": token_type_ids,
         """lengths""": input_lengths,
         """attention_mask""": input_mask,
     }
     return config, inputs_dict







@require_torch
class 		_A						( UpperCAmelCase_	,				UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					(
      (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  lowercase_						:			List[str]						=					(
      {
          '''feature-extraction''': FlaubertModel,
          '''fill-mask''': FlaubertWithLMHeadModel,
          '''question-answering''': FlaubertForQuestionAnsweringSimple,
          '''text-classification''': FlaubertForSequenceClassification,
          '''token-classification''': FlaubertForTokenClassification,
          '''zero-shot''': FlaubertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     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    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	Dict=False			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					super()._prepare_for_class(lowerCamelCase__   ,				lowerCamelCase__   ,				return_labels=lowerCamelCase__			)

     if return_labels:
        if model_class.__name__ == "FlaubertForQuestionAnswering":
           __UpperCamelCase	:			Tuple					=					torch.zeros(
               self.model_tester.batch_size   ,				dtype=torch.long   ,				device=lowerCamelCase__			)
           __UpperCamelCase	:			Tuple					=					torch.zeros(
               self.model_tester.batch_size   ,				dtype=torch.long   ,				device=lowerCamelCase__			)

     return inputs_dict

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					FlaubertModelTester(self			)
     __UpperCamelCase	:			Tuple					=					ConfigTester(self   ,				config_class=lowerCamelCase__   ,				emb_dim=37			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     self.config_tester.run_common_tests()

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_model(*lowerCamelCase__			)

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_lm_head(*lowerCamelCase__			)

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_simple_qa(*lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_qa(*lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCamelCase__			)

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_token_classif(*lowerCamelCase__			)

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_multiple_choice(*lowerCamelCase__			)

  @slow
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __UpperCamelCase	:			Union[str, Any]					=					FlaubertModel.from_pretrained(lowerCamelCase__			)
        self.assertIsNotNone(lowerCamelCase__			)

  @slow
  @require_torch_gpu
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			int					=					self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
        # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
        if model_class == FlaubertForMultipleChoice:
           return

        __UpperCamelCase	:			int					=					True
        __UpperCamelCase	:			Any					=					model_class(config=lowerCamelCase__			)

        __UpperCamelCase	:			List[str]					=					self._prepare_for_class(lowerCamelCase__   ,				lowerCamelCase__			)
        __UpperCamelCase	:			Tuple					=					torch.jit.trace(
            lowerCamelCase__   ,				(inputs_dict["""input_ids"""].to("""cpu"""			), inputs_dict["""attention_mask"""].to("""cpu"""			))			)

        with tempfile.TemporaryDirectory() as tmp:
           torch.jit.save(lowerCamelCase__   ,				os.path.join(lowerCamelCase__   ,				"""traced_model.pt"""			)			)
           __UpperCamelCase	:			Optional[int]					=					torch.jit.load(os.path.join(lowerCamelCase__   ,				"""traced_model.pt"""			)   ,				map_location=lowerCamelCase__			)
           loaded(inputs_dict["""input_ids"""].to(lowerCamelCase__			)   ,				inputs_dict["""attention_mask"""].to(lowerCamelCase__			)			)







@require_torch
class 		_A						( unittest.TestCase ):

  @slow
  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""			)
     __UpperCamelCase	:			List[str]					=					torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]			)
     with torch.no_grad():
        __UpperCamelCase	:			List[Any]					=					model(lowerCamelCase__			)[0]
     __UpperCamelCase	:			Tuple					=					torch.Size((1, 11, 7_68)			)
     self.assertEqual(output.shape   ,				lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					torch.tensor(
         [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]			)

     self.assertTrue(torch.allclose(output[:, :3, :3]   ,				lowerCamelCase__   ,				atol=1e-4			)			)











from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



