


'''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 				__lowerCamelCase				:
    """simple docstring"""


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


    def      A					(						self    :							Tuple):
           _A  :			Union[str, Any]											=      ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size)
           _A  :			Optional[Any]											=      random_attention_mask([self.batch_size, self.seq_length]   ,							dtype=tf.floataa)

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

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

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

           _A  :			Optional[int]											=      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      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							int   ,							):
           _A  :			Any											=      TFXLMModel(config=SCREAMING_SNAKE_CASE)
           _A  :			Optional[int]											=      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
           _A  :			int											=      model(SCREAMING_SNAKE_CASE)

           _A  :			int											=      [input_ids, input_mask]
           _A  :			int											=      model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size))


    def      A					(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							int   ,							):
           _A  :			List[str]											=      TFXLMWithLMHeadModel(SCREAMING_SNAKE_CASE)

           _A  :			List[str]											=      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
           _A  :			Tuple											=      model(SCREAMING_SNAKE_CASE)

           _A  :			Tuple											=      outputs

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


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							):
           _A  :			int											=      TFXLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE)

           _A  :			Optional[int]											=      {'input_ids': input_ids, 'lengths': input_lengths}

           _A  :			Union[str, Any]											=      model(SCREAMING_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    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							):
           _A  :			Tuple											=      TFXLMForSequenceClassification(SCREAMING_SNAKE_CASE)

           _A  :			Union[str, Any]											=      {'input_ids': input_ids, 'lengths': input_lengths}

           _A  :			Optional[int]											=      model(SCREAMING_SNAKE_CASE)

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


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							int   ,							):
           _A  :			List[str]											=      self.num_labels
           _A  :			Optional[int]											=      TFXLMForTokenClassification(config=SCREAMING_SNAKE_CASE)
           _A  :			Union[str, Any]											=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _A  :			int											=      model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.num_labels))


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							):
           _A  :			int											=      self.num_choices
           _A  :			Tuple											=      TFXLMForMultipleChoice(config=SCREAMING_SNAKE_CASE)
           _A  :			Union[str, Any]											=      tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE   ,							1)   ,							(1, self.num_choices, 1))
           _A  :			List[str]											=      tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE   ,							1)   ,							(1, self.num_choices, 1))
           _A  :			List[str]											=      tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE   ,							1)   ,							(1, self.num_choices, 1))
           _A  :			List[str]											=      {
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
               'token_type_ids': multiple_choice_token_type_ids,
           }
           _A  :			Union[str, Any]											=      model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_choices))


    def      A					(						self    :							Tuple):
           _A  :			List[Any]											=      self.prepare_config_and_inputs()
           (
               (
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				(
               _A
           )						,				
           )  :			List[Any]											=      config_and_inputs
           _A  :			Union[str, Any]											=      {
               'input_ids': input_ids,
               'token_type_ids': token_type_ids,
               'langs': token_type_ids,
               'lengths': input_lengths,
           }
           return config, inputs_dict




@require_tf
class 				__lowerCamelCase				( a_       ,      a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(
        (
            TFXLMModel,
            TFXLMWithLMHeadModel,
            TFXLMForSequenceClassification,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForTokenClassification,
            TFXLMForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a									=					(
        (TFXLMWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    a									=					(
        {
            "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 {}
    )
    a									=					False
    a									=					False


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							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      A					(						self    :							Any):
           _A  :			Optional[int]											=      TFXLMModelTester(self)
           _A  :			int											=      ConfigTester(self   ,							config_class=SCREAMING_SNAKE_CASE   ,							emb_dim=37)


    def      A					(						self    :							Any):
           self.config_tester.run_common_tests()


    def      A					(						self    :							Optional[Any]):
           _A  :			Dict											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Dict):
           _A  :			int											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str):
           _A  :			Union[str, Any]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           _A  :			List[Any]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           _A  :			List[Any]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_for_token_classification(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							List[Any]):
           _A  :			str											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE)


    @slow
    def      A					(						self    :							Any):
           for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A  :			Optional[Any]											=      TFXLMModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)




@require_tf
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @slow
    def      A					(						self    :							Dict):
           _A  :			Union[str, Any]											=      TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048')
           _A  :			List[Any]											=      tf.convert_to_tensor([[14, 447]]   ,							dtype=tf.intaa)  # the president
           _A  :			List[str]											=      [
               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
           _A  :			int											=      model.generate(SCREAMING_SNAKE_CASE   ,							do_sample=SCREAMING_SNAKE_CASE)
           self.assertListEqual(output_ids[0].numpy().tolist()   ,							SCREAMING_SNAKE_CASE)










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






