




from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
         TFConvBertForMaskedLM,
         TFConvBertForMultipleChoice,
         TFConvBertForQuestionAnswering,
         TFConvBertForSequenceClassification,
         TFConvBertForTokenClassification,
         TFConvBertModel,
     )






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=13      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=99      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=37      ,				__UpperCAmelCase="gelu"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=512      ,				__UpperCAmelCase=16      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=0.02      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=None      ,				):
        lowerCAmelCase__				:  Union[str, Any]		       =     parent
        lowerCAmelCase__				:  Union[str, Any]		       =     13
        lowerCAmelCase__				:  Tuple		       =     7
        lowerCAmelCase__				:  Optional[int]		       =     True
        lowerCAmelCase__				:  Union[str, Any]		       =     True
        lowerCAmelCase__				:  Optional[int]		       =     True
        lowerCAmelCase__				:  int		       =     True
        lowerCAmelCase__				:  Tuple		       =     99
        lowerCAmelCase__				:  List[Any]		       =     384
        lowerCAmelCase__				:  Optional[Any]		       =     2
        lowerCAmelCase__				:  Tuple		       =     4
        lowerCAmelCase__				:  Union[str, Any]		       =     37
        lowerCAmelCase__				:  int		       =     '''gelu'''
        lowerCAmelCase__				:  Optional[int]		       =     0.1
        lowerCAmelCase__				:  Union[str, Any]		       =     0.1
        lowerCAmelCase__				:  List[str]		       =     512
        lowerCAmelCase__				:  Tuple		       =     16
        lowerCAmelCase__				:  Dict		       =     2
        lowerCAmelCase__				:  Tuple		       =     0.02
        lowerCAmelCase__				:  int		       =     3
        lowerCAmelCase__				:  str		       =     4
        lowerCAmelCase__				:  Optional[Any]		       =     128
        lowerCAmelCase__				:  int		       =     2
        lowerCAmelCase__				:  Any		       =     9
        lowerCAmelCase__				:  Dict		       =     1
        lowerCAmelCase__				:  int		       =     None

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size		)

        lowerCAmelCase__				:  List[str]		       =     None
        if self.use_input_mask:
          lowerCAmelCase__				:  Dict		       =     random_attention_mask([self.batch_size, self.seq_length]		)

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

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

        lowerCAmelCase__				:  Dict		       =     ConvBertConfig(
            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=__UpperCAmelCase      ,				)

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     TFConvBertModel(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

        lowerCAmelCase__				:  Dict		       =     [input_ids, input_mask]
        lowerCAmelCase__				:  List[str]		       =     model(__UpperCAmelCase		)

        lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase		)

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFConvBertForMaskedLM(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     self.num_labels
        lowerCAmelCase__				:  str		       =     TFConvBertForSequenceClassification(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        lowerCAmelCase__				:  Optional[int]		       =     model(__UpperCAmelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     self.num_choices
        lowerCAmelCase__				:  List[Any]		       =     TFConvBertForMultipleChoice(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     tf.tile(tf.expand_dims(__UpperCAmelCase      ,				1		)      ,				(1, self.num_choices, 1)		)
        lowerCAmelCase__				:  Dict		       =     tf.tile(tf.expand_dims(__UpperCAmelCase      ,				1		)      ,				(1, self.num_choices, 1)		)
        lowerCAmelCase__				:  str		       =     tf.tile(tf.expand_dims(__UpperCAmelCase      ,				1		)      ,				(1, self.num_choices, 1)		)
        lowerCAmelCase__				:  Optional[int]		       =     {
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
            '''token_type_ids''': multiple_choice_token_type_ids,
        }
        lowerCAmelCase__				:  List[str]		       =     model(__UpperCAmelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[Any]		       =     self.num_labels
        lowerCAmelCase__				:  List[Any]		       =     TFConvBertForTokenClassification(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     TFConvBertForQuestionAnswering(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        lowerCAmelCase__				:  Dict		       =     model(__UpperCAmelCase		)
        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 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,			(
            lowerCAmelCase__					
        )       ,								
        )				:  int		       =     config_and_inputs
        lowerCAmelCase__				:  Optional[Any]		       =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






@require_tf
class  _lowerCAmelCase     (  _lowercase    ,			_lowercase    ,			unittest.TestCase	):
      A__		     =		(
          (
              TFConvBertModel,
              TFConvBertForMaskedLM,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      A__		     =		(
          {
              'feature-extraction': TFConvBertModel,
              'fill-mask': TFConvBertForMaskedLM,
              'question-answering': TFConvBertForQuestionAnswering,
              'text-classification': TFConvBertForSequenceClassification,
              'token-classification': TFConvBertForTokenClassification,
              'zero-shot': TFConvBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      A__		     =		False
      A__		     =		False
      A__		     =		False

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     TFConvBertModelTester(self		)
        lowerCAmelCase__				:  List[Any]		       =     ConfigTester(self      ,				config_class=__UpperCAmelCase      ,				hidden_size=37		)

      def 	__magic_name__(   self		):
        self.config_tester.run_common_tests()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__				:  Tuple		       =     True
        lowerCAmelCase__				:  List[Any]		       =     True

        if hasattr(__UpperCAmelCase      ,				'''use_cache'''		):
          lowerCAmelCase__				:  Dict		       =     True

        lowerCAmelCase__				:  Optional[int]		       =     getattr(self.model_tester      ,				'''encoder_seq_length'''      ,				self.model_tester.seq_length		)
        lowerCAmelCase__				:  Any		       =     getattr(self.model_tester      ,				'''key_length'''      ,				__UpperCAmelCase		)

        for model_class in self.all_model_classes:
          lowerCAmelCase__				:  Any		       =     self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__				:  Any		       =     model_class(__UpperCAmelCase		)
          lowerCAmelCase__				:  Any		       =     len(model(__UpperCAmelCase		)		)

          with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__UpperCAmelCase      ,				saved_model=__UpperCAmelCase		)
            lowerCAmelCase__				:  Optional[int]		       =     os.path.join(__UpperCAmelCase      ,				'''saved_model'''      ,				'''1'''		)
            lowerCAmelCase__				:  Tuple		       =     tf.keras.models.load_model(__UpperCAmelCase		)
            lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase		)

            if self.is_encoder_decoder:
              lowerCAmelCase__				:  Optional[Any]		       =     outputs['''encoder_hidden_states''']
              lowerCAmelCase__				:  Optional[int]		       =     outputs['''encoder_attentions''']
            else:
              lowerCAmelCase__				:  Any		       =     outputs['''hidden_states''']
              lowerCAmelCase__				:  Any		       =     outputs['''attentions''']

            self.assertEqual(len(__UpperCAmelCase		)      ,				__UpperCAmelCase		)

            lowerCAmelCase__				:  Optional[int]		       =     getattr(
                self.model_tester      ,				'''expected_num_hidden_layers'''      ,				self.model_tester.num_hidden_layers + 1		)

            self.assertEqual(len(__UpperCAmelCase		)      ,				__UpperCAmelCase		)
            self.assertListEqual(
                list(output_hidden_states[0].shape[-2:]		)      ,				[self.model_tester.seq_length, self.model_tester.hidden_size]      ,				)

            self.assertEqual(len(__UpperCAmelCase		)      ,				self.model_tester.num_hidden_layers		)
            self.assertListEqual(
                list(output_attentions[0].shape[-3:]		)      ,				[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]      ,				)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''		)
        self.assertIsNotNone(__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__				:  Dict		       =     True
        lowerCAmelCase__				:  List[str]		       =     getattr(self.model_tester      ,				'''decoder_seq_length'''      ,				self.model_tester.seq_length		)
        lowerCAmelCase__				:  int		       =     getattr(self.model_tester      ,				'''encoder_seq_length'''      ,				self.model_tester.seq_length		)
        lowerCAmelCase__				:  Tuple		       =     getattr(self.model_tester      ,				'''key_length'''      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     getattr(self.model_tester      ,				'''key_length'''      ,				__UpperCAmelCase		)

        def check_decoder_attentions_output(__UpperCAmelCase		):
          lowerCAmelCase__				:  List[Any]		       =     len(__UpperCAmelCase		)
          self.assertEqual(out_len % 2      ,				0		)
          lowerCAmelCase__				:  Any		       =     outputs.decoder_attentions
          self.assertEqual(len(__UpperCAmelCase		)      ,				self.model_tester.num_hidden_layers		)
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]		)      ,				[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]      ,				)

        def check_encoder_attentions_output(__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     [
              t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
          ]
          self.assertEqual(len(__UpperCAmelCase		)      ,				self.model_tester.num_hidden_layers		)
          self.assertListEqual(
              list(attentions[0].shape[-3:]		)      ,				[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]      ,				)

        for model_class in self.all_model_classes:
          lowerCAmelCase__				:  Optional[Any]		       =     True
          lowerCAmelCase__				:  Tuple		       =     False
          lowerCAmelCase__				:  Optional[int]		       =     model_class(__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[int]		       =     model(self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)
          lowerCAmelCase__				:  Tuple		       =     len(__UpperCAmelCase		)
          self.assertEqual(config.output_hidden_states      ,				__UpperCAmelCase		)
          check_encoder_attentions_output(__UpperCAmelCase		)

          if self.is_encoder_decoder:
            lowerCAmelCase__				:  Dict		       =     model_class(__UpperCAmelCase		)
            lowerCAmelCase__				:  List[Any]		       =     model(self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)
            self.assertEqual(config.output_hidden_states      ,				__UpperCAmelCase		)
            check_decoder_attentions_output(__UpperCAmelCase		)

          # Check that output attentions can also be changed via the config
          del inputs_dict["output_attentions"]
          lowerCAmelCase__				:  Any		       =     True
          lowerCAmelCase__				:  Union[str, Any]		       =     model_class(__UpperCAmelCase		)
          lowerCAmelCase__				:  str		       =     model(self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)
          self.assertEqual(config.output_hidden_states      ,				__UpperCAmelCase		)
          check_encoder_attentions_output(__UpperCAmelCase		)

          # Check attention is always last and order is fine
          lowerCAmelCase__				:  int		       =     True
          lowerCAmelCase__				:  Optional[Any]		       =     True
          lowerCAmelCase__				:  int		       =     model_class(__UpperCAmelCase		)
          lowerCAmelCase__				:  Dict		       =     model(self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)

          self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,				len(__UpperCAmelCase		)		)
          self.assertEqual(model.config.output_hidden_states      ,				__UpperCAmelCase		)
          check_encoder_attentions_output(__UpperCAmelCase		)






@require_tf
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''		)
        lowerCAmelCase__				:  List[str]		       =     tf.constant([[0, 1, 2, 3, 4, 5]]		)
        lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase		)[0]

        lowerCAmelCase__				:  Union[str, Any]		       =     [1, 6, 768]
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  Union[str, Any]		       =     tf.constant(
            [
                [
                    [-0.03475493, -0.4686034, -0.30638832],
                    [0.22637248, -0.26988646, -0.7423424],
                    [0.10324868, -0.45013508, -0.58280784],
                ]
            ]		)
        tf.debugging.assert_near(output[:, :3, :3]      ,				__UpperCAmelCase      ,				atol=1e-4		)












import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

