import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 (
      ErnieMForInformationExtraction,
      ErnieMForMultipleChoice,
      ErnieMForQuestionAnswering,
      ErnieMForSequenceClassification,
      ErnieMForTokenClassification,
      ErnieMModel,
  )
  from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=13							,     lowerCAmelCase_=7							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=99							,     lowerCAmelCase_=32							,     lowerCAmelCase_=5							,     lowerCAmelCase_=4							,     lowerCAmelCase_=37							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=5_12							,     lowerCAmelCase_=16							,     lowerCAmelCase_=2							,     lowerCAmelCase_=0.02							,     lowerCAmelCase_=3							,     lowerCAmelCase_=4							,     lowerCAmelCase_=None							,     )	->int:
        a  :    Tuple				=						parent
        a  :    Dict				=						batch_size
        a  :    Dict				=						seq_length
        a  :    List[str]				=						is_training
        a  :    Optional[Any]				=						use_input_mask
        a  :    Tuple				=						use_labels
        a  :    List[str]				=						vocab_size
        a  :    Dict				=						hidden_size
        a  :    Dict				=						num_hidden_layers
        a  :    Optional[int]				=						num_attention_heads
        a  :    Union[str, Any]				=						intermediate_size
        a  :    Any				=						hidden_act
        a  :    List[str]				=						hidden_dropout_prob
        a  :    List[str]				=						attention_probs_dropout_prob
        a  :    Union[str, Any]				=						max_position_embeddings
        a  :    Optional[int]				=						type_vocab_size
        a  :    Union[str, Any]				=						type_sequence_label_size
        a  :    Dict				=						initializer_range
        a  :    int				=						num_labels
        a  :    Union[str, Any]				=						num_choices
        a  :    List[str]				=						scope
   def _lowercase(    self    )	->Any:
        a  :    Tuple				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )

        a  :    Optional[int]				=						None
        if self.use_input_mask:
             a  :    str				=						random_attention_mask([self.batch_size, self.seq_length]    )

        a  :    Optional[Any]				=						None
        a  :    Any				=						None
        a  :    List[Any]				=						None
        if self.use_labels:
             a  :    Any				=						ids_tensor([self.batch_size]							,     self.type_sequence_label_size    )
             a  :    Optional[Any]				=						ids_tensor([self.batch_size, self.seq_length]							,     self.num_labels    )
             a  :    List[Any]				=						ids_tensor([self.batch_size]							,     self.num_choices    )

        a  :    str				=						self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )

        a  :    Union[str, Any]				=						None
        if self.use_input_mask:
             a  :    Dict				=						random_attention_mask([self.batch_size, self.seq_length]    )
        a  :    Dict				=						self.get_config()

        return config, input_ids, input_mask
   def _lowercase(    self    )	->Tuple:
        return ErnieMConfig(
            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							,     )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->int:
        a  :    Union[str, Any]				=						ErnieMModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        a  :    Optional[Any]				=						model(lowerCAmelCase_							,     return_dict=lowerCAmelCase_    )
        self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->int:
        a  :    str				=						ErnieMForQuestionAnswering(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        a  :    Union[str, Any]				=						model(
            lowerCAmelCase_							,     attention_mask=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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Union[str, Any]:
        a  :    Dict				=						ErnieMForInformationExtraction(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        a  :    Union[str, Any]				=						torch.ones_like(lowerCAmelCase_							,     dtype=torch.floataa    )
        a  :    Union[str, Any]				=						model(
            lowerCAmelCase_							,     attention_mask=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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Tuple:
        a  :    Optional[int]				=						self.num_labels
        a  :    List[Any]				=						ErnieMForSequenceClassification(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        a  :    Union[str, Any]				=						model(lowerCAmelCase_							,     attention_mask=lowerCAmelCase_							,     labels=lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Dict:
        a  :    Union[str, Any]				=						self.num_labels
        a  :    List[Any]				=						ErnieMForTokenClassification(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        input_ids.to(lowerCAmelCase_    )
        input_mask.to(lowerCAmelCase_    )
        token_labels.to(lowerCAmelCase_    )

        a  :    Union[str, Any]				=						model(lowerCAmelCase_							,     attention_mask=lowerCAmelCase_							,     labels=lowerCAmelCase_    )

        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.num_labels)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->str:
        a  :    Dict				=						self.num_choices
        a  :    Optional[int]				=						ErnieMForMultipleChoice(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()
        a  :    Any				=						input_ids.unsqueeze(1    ).expand(-1							,     self.num_choices							,     -1    ).contiguous()
        a  :    Tuple				=						input_mask.unsqueeze(1    ).expand(-1							,     self.num_choices							,     -1    ).contiguous()
        a  :    Union[str, Any]				=						model(
            lowerCAmelCase_							,     attention_mask=lowerCAmelCase_							,     labels=lowerCAmelCase_							,     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_choices)    )


   def _lowercase(    self    )	->Any:
        a  :    Optional[int]				=						self.prepare_config_and_inputs()
        (
            (
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							(
            a
        )			,							
        )  :    str				=						config_and_inputs
        a  :    Union[str, Any]				=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






@require_torch
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Union[str, Any] 			=   (
       (
           ErnieMModel,
           ErnieMForMultipleChoice,
           ErnieMForQuestionAnswering,
           ErnieMForSequenceClassification,
           ErnieMForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   UpperCamelCase_				:						List[Any] 			=   ()
   UpperCamelCase_				:						str 			=   (
       {
           'feature-extraction': ErnieMModel,
           'question-answering': ErnieMForQuestionAnswering,
           'text-classification': ErnieMForSequenceClassification,
           'token-classification': ErnieMForTokenClassification,
           'zero-shot': ErnieMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCamelCase_				:						Tuple 			=   False
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Optional[int]:
        if pipeline_test_casse_name == "QAPipelineTests":
             return True

        return False
   def _lowercase(    self    )	->Union[str, Any]:
        a  :    Dict				=						ErnieMModelTester(self    )
        a  :    Tuple				=						ConfigTester(self							,     config_class=lowerCAmelCase_							,     hidden_size=37    )
   def _lowercase(    self    )	->int:
        self.config_tester.run_common_tests()
   def _lowercase(    self    )	->Tuple:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Any:
        a  :    int				=						self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             a  :    int				=						type
             self.model_tester.create_and_check_model(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[int]:
        a  :    List[Any]				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_    )
   def _lowercase(    self    )	->List[str]:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[Any]:
        a  :    Dict				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_    )
   def _lowercase(    self    )	->str:
        a  :    Tuple				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCAmelCase_    )
   def _lowercase(    self    )	->List[Any]:
        a  :    str				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_    )


   @slow
   def _lowercase(    self    )	->Any:
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a  :    str				=						ErnieMModel.from_pretrained(lowerCAmelCase_    )
             self.assertIsNotNone(lowerCAmelCase_    )






@require_torch
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @slow
   def _lowercase(    self    )	->Dict:
        a  :    Optional[Any]				=						ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''    )
        model.eval()
        a  :    Tuple				=						torch.tensor([[0, 1, 2, 3, 4, 5]]    )
        a  :    Dict				=						model(lowerCAmelCase_    )[0]

        # TODO Replace vocab size
        a  :    Tuple				=						7_68

        a  :    int				=						torch.Size((1, 6, hidden_size)    )
        self.assertEqual(output.shape							,     lowerCAmelCase_    )

        a  :    Union[str, Any]				=						torch.tensor(
            [[[-0.00_12, 0.12_45, -0.02_14], [-0.07_42, 0.02_44, -0.07_71], [-0.03_33, 0.11_64, -0.15_54]]]    )

        self.assertTrue(torch.allclose(output[:, :3, :3]							,     lowerCAmelCase_							,     atol=1e-3    )    )
from __future__ import annotations

UpperCAmelCase							   =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_    )	->None:
        a  :    int				=						-1
        a  :    int				=						-1
        a  :    list[int]				=						[-1] * length
        a  :    Node | None				=						None
        a  :    Node | None				=						None


   def __repr__(    self    )	->str:
        return F"""Node(min_value={self.minn} max_value={self.maxx})"""







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Node | None:
     a  :    Dict				=						Node(len(lowerCAmelCase_       )       )
     a			,							a  :    Tuple				=						min(lowerCAmelCase_       ), max(lowerCAmelCase_       )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     a  :    Optional[Any]				=						(root.minn + root.maxx) // 2

     a  :    list[int]				=						[]
     a  :    list[int]				=						[]

     for index, num in enumerate(lowerCAmelCase_       ):
          if num <= pivot:
               left_arr.append(lowerCAmelCase_       )
          else:
               right_arr.append(lowerCAmelCase_       )
          a  :    int				=						len(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     return root







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     a  :    List[Any]				=						(node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left						,      lowerCAmelCase_						,      node.map_left[index] - 1       )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right						,      lowerCAmelCase_						,      index - node.map_left[index]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if start > end:
          return 0
     a  :    Optional[Any]				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Any				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      start - 1       )
     return rank_till_end - rank_before_start







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     a  :    Optional[int]				=						node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left						,      lowerCAmelCase_						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      )
     else:
          return quantile(
              node.right						,      index - num_elements_in_left_tree						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     a  :    Tuple				=						range_counting(
         node.left						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     a  :    int				=						range_counting(
         node.right						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()