




import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
         DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         DistilBertForMaskedLM,
         DistilBertForMultipleChoice,
         DistilBertForQuestionAnswering,
         DistilBertForSequenceClassification,
         DistilBertForTokenClassification,
         DistilBertModel,
     )






class  _lowerCAmelCase     (  snake_case__	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=13      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=99      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase=5      ,				__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__				:  Any		       =     parent
        lowerCAmelCase__				:  Any		       =     batch_size
        lowerCAmelCase__				:  Optional[int]		       =     seq_length
        lowerCAmelCase__				:  Optional[int]		       =     is_training
        lowerCAmelCase__				:  Optional[int]		       =     use_input_mask
        lowerCAmelCase__				:  Tuple		       =     use_token_type_ids
        lowerCAmelCase__				:  Optional[int]		       =     use_labels
        lowerCAmelCase__				:  Optional[int]		       =     vocab_size
        lowerCAmelCase__				:  List[str]		       =     hidden_size
        lowerCAmelCase__				:  str		       =     num_hidden_layers
        lowerCAmelCase__				:  str		       =     num_attention_heads
        lowerCAmelCase__				:  List[str]		       =     intermediate_size
        lowerCAmelCase__				:  Optional[Any]		       =     hidden_act
        lowerCAmelCase__				:  List[Any]		       =     hidden_dropout_prob
        lowerCAmelCase__				:  Any		       =     attention_probs_dropout_prob
        lowerCAmelCase__				:  Optional[Any]		       =     max_position_embeddings
        lowerCAmelCase__				:  Optional[int]		       =     type_vocab_size
        lowerCAmelCase__				:  Union[str, Any]		       =     type_sequence_label_size
        lowerCAmelCase__				:  Dict		       =     initializer_range
        lowerCAmelCase__				:  int		       =     num_labels
        lowerCAmelCase__				:  List[str]		       =     num_choices
        lowerCAmelCase__				:  Dict		       =     scope

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

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

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

        lowerCAmelCase__				:  int		       =     self.get_config()

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

      def 	__magic_name__(   self		):
        return DistilBertConfig(
            vocab_size=self.vocab_size      ,				dim=self.hidden_size      ,				n_layers=self.num_hidden_layers      ,				n_heads=self.num_attention_heads      ,				hidden_dim=self.intermediate_size      ,				hidden_act=self.hidden_act      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     DistilBertModel(config=UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        lowerCAmelCase__				:  Dict		       =     model(UpperCAmelCase_      ,				UpperCAmelCase_		)
        lowerCAmelCase__				:  int		       =     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		):
        lowerCAmelCase__				:  str		       =     DistilBertForMaskedLM(config=UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        lowerCAmelCase__				:  Optional[Any]		       =     model(UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				labels=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		):
        lowerCAmelCase__				:  Optional[Any]		       =     DistilBertForQuestionAnswering(config=UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        lowerCAmelCase__				:  Tuple		       =     model(
            UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				start_positions=UpperCAmelCase_      ,				end_positions=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      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self.num_labels
        lowerCAmelCase__				:  Any		       =     DistilBertForSequenceClassification(UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        lowerCAmelCase__				:  Union[str, Any]		       =     model(UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				labels=UpperCAmelCase_		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     self.num_labels
        lowerCAmelCase__				:  Tuple		       =     DistilBertForTokenClassification(config=UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()

        lowerCAmelCase__				:  int		       =     model(UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				labels=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		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.num_choices
        lowerCAmelCase__				:  Dict		       =     DistilBertForMultipleChoice(config=UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        lowerCAmelCase__				:  Optional[Any]		       =     input_ids.unsqueeze(1		).expand(-1      ,				self.num_choices      ,				-1		).contiguous()
        lowerCAmelCase__				:  Dict		       =     input_mask.unsqueeze(1		).expand(-1      ,				self.num_choices      ,				-1		).contiguous()
        lowerCAmelCase__				:  List[str]		       =     model(
            UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				labels=UpperCAmelCase_      ,				)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.prepare_config_and_inputs()
        ((lowerCAmelCase__)       ,			(lowerCAmelCase__)       ,			(lowerCAmelCase__)       ,			(lowerCAmelCase__)       ,			(lowerCAmelCase__)       ,			(lowerCAmelCase__))				:  Union[str, Any]		       =     config_and_inputs
        lowerCAmelCase__				:  Dict		       =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






@require_torch
class  _lowerCAmelCase     (  snake_case__    ,			snake_case__    ,			unittest.TestCase	):
      A__		     =		(
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      A__		     =		(
          {
              """feature-extraction""": DistilBertModel,
              """fill-mask""": DistilBertForMaskedLM,
              """question-answering""": DistilBertForQuestionAnswering,
              """text-classification""": DistilBertForSequenceClassification,
              """token-classification""": DistilBertForTokenClassification,
              """zero-shot""": DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A__		     =		True
      A__		     =		True
      A__		     =		True
      A__		     =		True

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     DistilBertModelTester(self		)
        lowerCAmelCase__				:  str		       =     ConfigTester(self      ,				config_class=UpperCAmelCase_      ,				dim=37		)

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase_		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase_		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase_		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase_		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase_		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase_		)

      @slow
      def 	__magic_name__(   self		):



        for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__				:  Optional[int]		       =     DistilBertModel.from_pretrained(UpperCAmelCase_		)
          self.assertIsNotNone(UpperCAmelCase_		)







      @slow
      @require_torch_gpu
      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
          # BertForMultipleChoice behaves incorrectly in JIT environments.
          if model_class == DistilBertForMultipleChoice:
            return

          lowerCAmelCase__				:  Any		       =     True
          lowerCAmelCase__				:  int		       =     model_class(config=UpperCAmelCase_		)

          lowerCAmelCase__				:  Optional[Any]		       =     self._prepare_for_class(UpperCAmelCase_      ,				UpperCAmelCase_		)
          lowerCAmelCase__				:  List[Any]		       =     torch.jit.trace(
              UpperCAmelCase_      ,				(inputs_dict['''input_ids'''].to('''cpu'''		), inputs_dict['''attention_mask'''].to('''cpu'''		))		)

          with tempfile.TemporaryDirectory() as tmp:
            torch.jit.save(UpperCAmelCase_      ,				os.path.join(UpperCAmelCase_      ,				'''traced_model.pt'''		)		)
            lowerCAmelCase__				:  Any		       =     torch.jit.load(os.path.join(UpperCAmelCase_      ,				'''traced_model.pt'''		)      ,				map_location=UpperCAmelCase_		)
            loaded(inputs_dict['''input_ids'''].to(UpperCAmelCase_		)      ,				inputs_dict['''attention_mask'''].to(UpperCAmelCase_		)		)






@require_torch
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     DistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
        lowerCAmelCase__				:  Any		       =     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
        lowerCAmelCase__				:  Dict		       =     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        with torch.no_grad():
          lowerCAmelCase__				:  Any		       =     model(UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_		)[0]
        lowerCAmelCase__				:  Optional[Any]		       =     torch.Size((1, 11, 768)		)
        self.assertEqual(output.shape      ,				UpperCAmelCase_		)
        lowerCAmelCase__				:  Optional[Any]		       =     torch.tensor(
            [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]		)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,				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)

