

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 (
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerModel,
      )
      from transformers.models.roformer.modeling_tf_roformer import (
          TFRoFormerSelfAttention,
          TFRoFormerSinusoidalPositionalEmbedding,
      )




class      UpperCamelCase :

    def __init__(			self :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Any=1_3							,    snake_case__ :      Tuple=7							,    snake_case__ :      Tuple=True							,    snake_case__ :      int=True							,    snake_case__ :      List[str]=True							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      Optional[Any]=9_9							,    snake_case__ :      List[str]=3_2							,    snake_case__ :      int=2							,    snake_case__ :      Optional[int]=4							,    snake_case__ :      Any=3_7							,    snake_case__ :      Any="gelu"							,    snake_case__ :      List[Any]=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      Dict=5_1_2							,    snake_case__ :      Tuple=1_6							,    snake_case__ :      int=2							,    snake_case__ :      int=0.02							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Optional[Any]=4							,    snake_case__ :      Optional[Any]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			1_3
     SCREAMING_SNAKE_CASE								=			7
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			9_9
     SCREAMING_SNAKE_CASE								=			3_2
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			4
     SCREAMING_SNAKE_CASE								=			3_7
     SCREAMING_SNAKE_CASE								=			'gelu'
     SCREAMING_SNAKE_CASE								=			0.1
     SCREAMING_SNAKE_CASE								=			0.1
     SCREAMING_SNAKE_CASE								=			5_1_2
     SCREAMING_SNAKE_CASE								=			1_6
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			0.02
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			4
     SCREAMING_SNAKE_CASE								=			None

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

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

     SCREAMING_SNAKE_CASE								=			RoFormerConfig(
         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							,    return_dict=snake_case__							,    )

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerModel(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

     SCREAMING_SNAKE_CASE								=			[input_ids, input_mask]
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )

     SCREAMING_SNAKE_CASE								=			model(snake_case__    )

     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]							,    snake_case__ :      str							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			TFRoFormerForCausalLM(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )['logits']
     self.parent.assertListEqual(
         list(prediction_scores.numpy().shape    )							,    [self.batch_size, self.seq_length, self.vocab_size]    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerForMaskedLM(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      str							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			TFRoFormerForSequenceClassification(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]							,    snake_case__ :      str							,    snake_case__ :      Dict							,    snake_case__ :      Tuple							,    snake_case__ :      List[Any]							,    snake_case__ :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_choices
     SCREAMING_SNAKE_CASE								=			TFRoFormerForMultipleChoice(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tf.tile(tf.expand_dims(snake_case__							,    1    )							,    (1, self.num_choices, 1)    )
     SCREAMING_SNAKE_CASE								=			tf.tile(tf.expand_dims(snake_case__							,    1    )							,    (1, self.num_choices, 1)    )
     SCREAMING_SNAKE_CASE								=			tf.tile(tf.expand_dims(snake_case__							,    1    )							,    (1, self.num_choices, 1)    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': multiple_choice_inputs_ids,
         'attention_mask': multiple_choice_input_mask,
         'token_type_ids': multiple_choice_token_type_ids,
     }
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      str							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			TFRoFormerForTokenClassification(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerForQuestionAnswering(config=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     SCREAMING_SNAKE_CASE								=			model(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 UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_tf
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __UpperCamelCase         =(
        {
            "feature-extraction": TFRoFormerModel,
            "fill-mask": TFRoFormerForMaskedLM,
            "question-answering": TFRoFormerForQuestionAnswering,
            "text-classification": TFRoFormerForSequenceClassification,
            "text-generation": TFRoFormerForCausalLM,
            "token-classification": TFRoFormerForTokenClassification,
            "zero-shot": TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Any							,    snake_case__ :      Dict    ):

     """simple docstring"""






     if pipeline_test_casse_name == "TextGenerationPipelineTests":
      return True

     return False

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__    )




    @slow
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'    )
     self.assertIsNotNone(snake_case__    )




@require_tf
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
     SCREAMING_SNAKE_CASE								=			tf.constant([[0, 1, 2, 3, 4, 5]]    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )[0]

     # TODO Replace vocab size
     SCREAMING_SNAKE_CASE								=			5_0_0_0_0

     SCREAMING_SNAKE_CASE								=			[1, 6, vocab_size]
     self.assertEqual(output.shape							,    snake_case__    )

     print(output[:, :3, :3]    )

     # TODO Replace values below with what was printed above.
     SCREAMING_SNAKE_CASE								=			tf.constant(
         [
             [
                 [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                 [-1.5_133_783, 0.197_433, 0.15_190_607],
                 [-5.0_135_403, -3.900_256, -0.84_038_764],
             ]
         ]    )
     tf.debugging.assert_near(output[:, :3, :3]							,    snake_case__							,    atol=1E-4    )




@require_tf
class      UpperCamelCase (     unittest.TestCase   ):
    __UpperCamelCase         =1e-4

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tf.constant([[4, 1_0]]    )
     SCREAMING_SNAKE_CASE								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,    embedding_dim=6    )

     SCREAMING_SNAKE_CASE								=			emba(input_ids.shape    )
     SCREAMING_SNAKE_CASE								=			tf.constant(
         [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]    )

     tf.debugging.assert_near(snake_case__							,    snake_case__							,    atol=self.tolerance    )




    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tf.constant(
         [
             [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
             [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
             [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
         ]    )
     SCREAMING_SNAKE_CASE								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2							,    embedding_dim=5_1_2    )
     emba([2, 1_6, 5_1_2]    )
     SCREAMING_SNAKE_CASE								=			emba.weight[:3, :5]

     tf.debugging.assert_near(snake_case__							,    snake_case__							,    atol=self.tolerance    )




@require_tf
class      UpperCamelCase (     unittest.TestCase   ):
    __UpperCamelCase         =1e-4

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4							,    dtype=tf.floataa    )							,    shape=(2, 1_2, 1_6, 6_4)    ) / 1_0_0

     SCREAMING_SNAKE_CASE								=			-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4							,    dtype=tf.floataa    )							,    shape=(2, 1_2, 1_6, 6_4)    ) / 1_0_0

     SCREAMING_SNAKE_CASE								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2							,    embedding_dim=6_4    )
     SCREAMING_SNAKE_CASE								=			embed_positions([2, 1_6, 7_6_8]    )[None, None, :, :]

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
         snake_case__							,    snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			tf.constant(
         [
             [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
             [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
             [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
             [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
             [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
             [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
         ]    )
     SCREAMING_SNAKE_CASE								=			tf.constant(
         [
             [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
             [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
             [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
             [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
             [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
             [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
         ]    )

     tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,    snake_case__							,    atol=self.tolerance    )
     tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,    snake_case__							,    atol=self.tolerance    )




import heapq
import sys

import numpy as np

a_    :						Optional[int]    		=      tuple[int, int]




class      UpperCamelCase :

    def __init__(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			set()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     if not self.empty():
      return self.elements[0][0]
     else:
      return float('inf'    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     return len(self.elements    ) == 0

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     if item not in self.set:
      heapq.heappush(self.elements							,    (priority, item)    )
      self.set.add(snake_case__    )
     else:
      # update
      # print("update", item)
      SCREAMING_SNAKE_CASE								=			[]
      ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
      while x != item:
       temp.append((pri, x)    )
       ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
      temp.append((priority, item)    )
      for pro, xxx in temp:
       heapq.heappush(self.elements							,    (pro, xxx)    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






     if item in self.set:
      self.set.remove(snake_case__    )
      SCREAMING_SNAKE_CASE								=			[]
      ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
      while x != item:
       temp.append((pro, x)    )
       ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
      for prito, yyy in temp:
       heapq.heappush(self.elements							,    (prito, yyy)    )

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return self.elements[0][1]




    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			heapq.heappop(self.elements    )
     self.set.remove(snake_case__    )
     return (priority, item)


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)
 return np.linalg.norm(a - b	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Dict:
 '''simple docstring'''


 return consistent_heuristic(_UpperCamelCase		,							_UpperCamelCase	) // t


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos	)		->							Optional[int]:
 '''simple docstring'''


 return abs(p[0] - goal[0]	) + abs(p[1] - goal[1]	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  TPos		,							_UpperCamelCase		:  dict[TPos, float]	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			g_function[start] + Wa * heuristics[i](_UpperCamelCase		,							_UpperCamelCase	)
 return ans


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  Tuple	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			np.chararray((n, n)	)
 for i in range(_UpperCamelCase	):
  for j in range(_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			'*'

 for i in range(_UpperCamelCase	):
  for j in range(_UpperCamelCase	):
   if (j, (n - 1) - i) in blocks:
    SCREAMING_SNAKE_CASE								=			'#'

 SCREAMING_SNAKE_CASE								=			'-'
 SCREAMING_SNAKE_CASE								=			back_pointer[goal]
 while x != start:
  ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			x
  # print(x)
  SCREAMING_SNAKE_CASE								=			'-'
  SCREAMING_SNAKE_CASE								=			back_pointer[x]
 SCREAMING_SNAKE_CASE								=			'-'

 for i in range(_UpperCamelCase	):
  for j in range(_UpperCamelCase	):
   if (i, j) == (0, n - 1):
    print(grid[i][j]		,							end=' '	)
    print('<-- End position'		,							end=' '	)
   else:
    print(grid[i][j]		,							end=' '	)
  print()
 print('^'	)
 print('Start position'	)
 print()
 print('# is an obstacle'	)
 print('- is the path taken by algorithm'	)
 print('PATH TAKEN BY THE ALGORITHM IS:-'	)
 SCREAMING_SNAKE_CASE								=			back_pointer[goal]
 while x != start:
  print(_UpperCamelCase		,							end=' '	)
  SCREAMING_SNAKE_CASE								=			back_pointer[x]
 print(_UpperCamelCase	)
 sys.exit()


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos	)		->							Any:
 '''simple docstring'''


 if p[0] < 0 or p[0] > n - 1:
  return False
 if p[1] < 0 or p[1] > n - 1:
  return False
 return True


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[Any]		,							)		->							List[Any]:
 '''simple docstring'''


 for itera in range(_UpperCamelCase	):
  open_list[itera].remove_element(_UpperCamelCase	)
 # print("s", s)
 # print("j", j)
 ((SCREAMING_SNAKE_CASE)						,	(SCREAMING_SNAKE_CASE))								=			s
 SCREAMING_SNAKE_CASE								=			(x - 1, y)
 SCREAMING_SNAKE_CASE								=			(x + 1, y)
 SCREAMING_SNAKE_CASE								=			(x, y + 1)
 SCREAMING_SNAKE_CASE								=			(x, y - 1)

 for neighbours in [left, right, up, down]:
  if neighbours not in blocks:
   if valid(_UpperCamelCase	) and neighbours not in visited:
    # print("neighbour", neighbours)
    visited.add(_UpperCamelCase	)
    SCREAMING_SNAKE_CASE								=			-1
    SCREAMING_SNAKE_CASE								=			float('inf'	)

   if valid(_UpperCamelCase	) and g_function[neighbours] > g_function[s] + 1:
    SCREAMING_SNAKE_CASE								=			g_function[s] + 1
    SCREAMING_SNAKE_CASE								=			s
    if neighbours not in close_list_anchor:
     open_list[0].put(_UpperCamelCase		,							key(_UpperCamelCase		,							0		,							_UpperCamelCase		,							_UpperCamelCase	)	)
     if neighbours not in close_list_inad:
      for var in range(1		,							_UpperCamelCase	):
       if key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	) <= Wa * key(
           _UpperCamelCase		,							0		,							_UpperCamelCase		,							_UpperCamelCase	):
        open_list[j].put(
            _UpperCamelCase		,							key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)	)


def        __lowerCAmelCase    (       )		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 for x in range(1		,							5	):
  for y in range(1		,							6	):
   some_list.append((x, y)	)

 for x in range(15		,							20	):
  some_list.append((x, 17)	)

 for x in range(10		,							19	):
  for y in range(1		,							15	):
   some_list.append((x, y)	)

    # L block
 for x in range(1		,							4	):
  for y in range(12		,							19	):
   some_list.append((x, y)	)
 for x in range(3		,							13	):
  for y in range(16		,							19	):
   some_list.append((x, y)	)
 return some_list


a_    :						str    		=      {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a_    :						List[str]    		=      [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a_    :						Union[str, Any]    		=      make_common_ground()


a_    :						Tuple    		=      blocks_blk
# hyper parameters
a_    :						Any    		=      1
a_    :						List[str]    		=      1
a_    :						Union[str, Any]    		=      20
a_    :						Optional[Any]    		=      3  # one consistent and two other inconsistent

# start and end destination
a_    :						int    		=      (0, 0)
a_    :						Optional[int]    		=      (n - 1, n - 1)

a_    :						Union[str, Any]    		=      1


def        __lowerCAmelCase    (       _UpperCamelCase		:  TPos		,							_UpperCamelCase		:  TPos		,							_UpperCamelCase		:  int	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{start: 0, goal: float('inf'	)}
 SCREAMING_SNAKE_CASE								=			{start: -1, goal: -1}
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			set()

 for i in range(_UpperCamelCase	):
  open_list.append(PriorityQueue()	)
  open_list[i].put(_UpperCamelCase		,							key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)	)

 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			[]
 while open_list[0].minkey() < float('inf'	):
  for i in range(1		,							_UpperCamelCase	):
   # print(open_list[0].minkey(), open_list[i].minkey())
   if open_list[i].minkey() <= Wa * open_list[0].minkey():
    global t
    t += 1
    if g_function[goal] <= open_list[i].minkey():
     if g_function[goal] < float('inf'	):
      do_something(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
    else:
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			open_list[i].top_show()
     visited.add(_UpperCamelCase	)
     expand_state(
         _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							)
     close_list_inad.append(_UpperCamelCase	)
   else:
    if g_function[goal] <= open_list[0].minkey():
     if g_function[goal] < float('inf'	):
      do_something(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
    else:
     SCREAMING_SNAKE_CASE								=			open_list[0].top_show()
     visited.add(_UpperCamelCase	)
     expand_state(
         _UpperCamelCase		,							0		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							)
     close_list_anchor.append(_UpperCamelCase	)
 print('No path found to goal'	)
 print()
 for i in range(n - 1		,							-1		,							-1	):
  for j in range(_UpperCamelCase	):
   if (j, i) in blocks:
    print('#'		,							end=' '	)
   elif (j, i) in back_pointer:
    if (j, i) == (n - 1, n - 1):
     print('*'		,							end=' '	)
    else:
     print('-'		,							end=' '	)
   else:
    print('*'		,							end=' '	)
   if (j, i) == (n - 1, n - 1):
    print('<-- End position'		,							end=' '	)
  print()
 print('^'	)
 print('Start position'	)
 print()
 print('# is an obstacle'	)
 print('- is the path taken by algorithm'	)


if __name__ == "__main__":
      multi_a_star(start, goal, n_heuristic)

