




'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import 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 (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMModel,
          TFXLMWithLMHeadModel,
          XLMConfig,
      )







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a       ,)			->							Tuple:
          snake_case :			str					   = parent
          snake_case :			Optional[Any]					   = 13
          snake_case :			str					   = 7
          snake_case :			int					   = True
          snake_case :			List[str]					   = True
          snake_case :			Optional[int]					   = True
          snake_case :			Any					   = True
          snake_case :			Dict					   = True
          snake_case :			List[Any]					   = False
          snake_case :			Any					   = False
          snake_case :			Optional[Any]					   = False
          snake_case :			int					   = 2
          snake_case :			List[str]					   = 99
          snake_case :			Optional[Any]					   = 0
          snake_case :			Any					   = 32
          snake_case :			List[str]					   = 2
          snake_case :			Dict					   = 4
          snake_case :			Any					   = 0.1
          snake_case :			Any					   = 0.1
          snake_case :			Dict					   = 512
          snake_case :			List[Any]					   = 16
          snake_case :			Optional[int]					   = 2
          snake_case :			int					   = 0.02
          snake_case :			str					   = 3
          snake_case :			Any					   = 4
          snake_case :			Optional[Any]					   = """last"""
          snake_case :			Union[str, Any]					   = True
          snake_case :			List[str]					   = None
          snake_case :			List[Any]					   = 0







     def  snake_case_			(							self       )			->							Dict:
          snake_case :			Dict					   = ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size       )
          snake_case :			Optional[Any]					   = random_attention_mask([self.batch_size, self.seq_length]       ,dtype=tf.floataa       )

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

          snake_case :			List[Any]					   = None
          if self.use_token_type_ids:
               snake_case :			Optional[Any]					   = ids_tensor([self.batch_size, self.seq_length]       ,self.n_langs       )

          snake_case :			Tuple					   = None
          snake_case :			int					   = None
          snake_case :			int					   = None
          if self.use_labels:
               snake_case :			int					   = ids_tensor([self.batch_size]       ,self.type_sequence_label_size       )
               snake_case :			Optional[Any]					   = ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels       )
               snake_case :			Dict					   = ids_tensor([self.batch_size]       ,2       ,dtype=tf.floataa       )
               snake_case :			str					   = ids_tensor([self.batch_size]       ,self.num_choices       )

          snake_case :			Union[str, Any]					   = 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       ,bos_token_id=self.bos_token_id       ,)

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







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							Tuple:
          snake_case :			Union[str, Any]					   = TFXLMModel(config=__a       )
          snake_case :			Any					   = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
          snake_case :			int					   = model(__a       )

          snake_case :			List[str]					   = [input_ids, input_mask]
          snake_case :			List[str]					   = model(__a       )
          self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							List[Any]:
          snake_case :			List[Any]					   = TFXLMWithLMHeadModel(__a       )

          snake_case :			List[str]					   = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
          snake_case :			Tuple					   = model(__a       )

          snake_case :			Tuple					   = outputs

          self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							Dict:
          snake_case :			Any					   = TFXLMForQuestionAnsweringSimple(__a       )

          snake_case :			List[Any]					   = {"""input_ids""": input_ids, """lengths""": input_lengths}

          snake_case :			Optional[int]					   = model(__a       )

          self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length)       )
          self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length)       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							str:
          snake_case :			int					   = TFXLMForSequenceClassification(__a       )

          snake_case :			Dict					   = {"""input_ids""": input_ids, """lengths""": input_lengths}

          snake_case :			Optional[int]					   = model(__a       )

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







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							List[str]:
          snake_case :			str					   = self.num_labels
          snake_case :			Tuple					   = TFXLMForTokenClassification(config=__a       )
          snake_case :			str					   = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          snake_case :			List[str]					   = model(__a       )
          self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,)			->							List[str]:
          snake_case :			Optional[int]					   = self.num_choices
          snake_case :			int					   = TFXLMForMultipleChoice(config=__a       )
          snake_case :			Optional[Any]					   = tf.tile(tf.expand_dims(__a       ,1       )       ,(1, self.num_choices, 1)       )
          snake_case :			List[Any]					   = tf.tile(tf.expand_dims(__a       ,1       )       ,(1, self.num_choices, 1)       )
          snake_case :			Union[str, Any]					   = tf.tile(tf.expand_dims(__a       ,1       )       ,(1, self.num_choices, 1)       )
          snake_case :			List[str]					   = {
              """input_ids""": multiple_choice_inputs_ids,
              """attention_mask""": multiple_choice_input_mask,
              """token_type_ids""": multiple_choice_token_type_ids,
          }
          snake_case :			str					   = model(__a       )
          self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices)       )







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Union[str, Any]					   = self.prepare_config_and_inputs()
          (
              (
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		(
              snake_case
          )   ,		
          ) :			List[Any]					   = config_and_inputs
          snake_case :			Dict					   = {
              """input_ids""": input_ids,
              """token_type_ids""": token_type_ids,
              """langs""": token_type_ids,
              """lengths""": input_lengths,
          }
          return config, inputs_dict







@require_tf
class 				_a   (a__,		a__,		unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[Any]									=				(
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowerCAmelCase_     :    List[str]									=				(
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     lowerCAmelCase_     :    Any									=				(
         {
             """feature-extraction""": TFXLMModel,
             """fill-mask""": TFXLMWithLMHeadModel,
             """question-answering""": TFXLMForQuestionAnsweringSimple,
             """text-classification""": TFXLMForSequenceClassification,
             """text-generation""": TFXLMWithLMHeadModel,
             """token-classification""": TFXLMForTokenClassification,
             """zero-shot""": TFXLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase_     :    Union[str, Any]									=				False
     lowerCAmelCase_     :    int									=				False







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       )			->							Union[str, Any]:
          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  snake_case_			(							self       )			->							List[str]:
          snake_case :			Any					   = TFXLMModelTester(self       )
          snake_case :			Optional[Any]					   = ConfigTester(self       ,config_class=__a       ,emb_dim=37       )







     def  snake_case_			(							self       )			->							Tuple:
          self.config_tester.run_common_tests()







     def  snake_case_			(							self       )			->							int:
          snake_case :			Optional[Any]					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*__a       )







     def  snake_case_			(							self       )			->							str:
          snake_case :			str					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*__a       )







     def  snake_case_			(							self       )			->							Tuple:
          snake_case :			Dict					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*__a       )







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			List[str]					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*__a       )







     def  snake_case_			(							self       )			->							Any:
          snake_case :			Optional[int]					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*__a       )







     def  snake_case_			(							self       )			->							Optional[int]:
          snake_case :			Dict					   = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*__a       )







     @slow
     def  snake_case_			(							self       )			->							Optional[int]:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               snake_case :			Union[str, Any]					   = TFXLMModel.from_pretrained(__a       )
               self.assertIsNotNone(__a       )







@require_tf
class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     @slow
     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Optional[int]					   = TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""       )
          snake_case :			Union[str, Any]					   = tf.convert_to_tensor([[14, 447]]       ,dtype=tf.intaa       )  # the president
          snake_case :			Union[str, Any]					   = [
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # 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
          snake_case :			Tuple					   = model.generate(__a       ,do_sample=__a       )
          self.assertListEqual(output_ids[0].numpy().tolist()       ,__a       )









'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase			:      Union[str, Any]			=					TypeVar("""T""", bound=bool)







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a       )			->							None:
          snake_case :			T					   = value
          snake_case :			SkewNode[T] | None					   = None
          snake_case :			SkewNode[T] | None					   = None







     @property
     def  snake_case_			(							self       )			->							T:
          return self._value







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							SkewNode[T] | None:
          if not roota:
               return roota

          if not roota:
               return roota

          if roota.value > roota.value:
               snake_case   ,		snake_case :			Tuple					   = roota, roota

          snake_case :			Tuple					   = roota
          snake_case :			Union[str, Any]					   = roota.right
          snake_case :			Any					   = roota.left
          snake_case :			Union[str, Any]					   = SkewNode.merge(__a       ,__a       )

          return result







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a = ()       )			->							None:
          snake_case :			SkewNode[T] | None					   = None
          if data:
               for item in data:
                    self.insert(__a       )







     def __bool__(							self       )			->							bool:
          return self._root is not None







     def __iter__(							self       )			->							Iterator[T]:
          snake_case :			list[Any]					   = []
          while self:
               result.append(self.pop()       )

          # Pushing items back to the heap not to clear it.
          for item in result:
               self.insert(__a       )

          return iter(__a       )







     def  snake_case_			(							self       ,__a       )			->							None:
          snake_case :			List[Any]					   = SkewNode.merge(self._root       ,SkewNode(__a       )       )







     def  snake_case_			(							self       )			->							T | None:
          snake_case :			Tuple					   = self.top()
          snake_case :			int					   = (
              SkewNode.merge(self._root.left       ,self._root.right       ) if self._root else None
          )

          return result







     def  snake_case_			(							self       )			->							T:
          if not self._root:
               raise IndexError("""Can't get top element for the empty heap."""       )
          return self._root.value







     def  snake_case_			(							self       )			->							None:
          snake_case :			Any					   = None


if __name__ == "__main__":
      import doctest

      doctest.testmod()



