





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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def __init__(				self							:Union[str, Any]      ,   snake_case							:List[Any]      ,   snake_case							:Optional[Any]=13      ,   snake_case							:List[Any]=7      ,   snake_case							:Optional[Any]=True      ,   snake_case							:List[Any]=True      ,   snake_case							:Optional[Any]=False      ,   snake_case							:Tuple=True      ,   snake_case							:Any=99      ,   snake_case							:Optional[int]=32      ,   snake_case							:Union[str, Any]=5      ,   snake_case							:Optional[Any]=4      ,   snake_case							:Optional[Any]=37      ,   snake_case							:Any="gelu"      ,   snake_case							:str=0.1      ,   snake_case							:Dict=0.1      ,   snake_case							:str=512      ,   snake_case							:Tuple=16      ,   snake_case							:int=2      ,   snake_case							:int=0.02      ,   snake_case							:Any=3      ,   snake_case							:Tuple=4      ,   snake_case							:int=None      ,   ):





        '''simple docstring'''




        A_       :					str        =      parent
        A_       :					Optional[int]        =      batch_size
        A_       :					int        =      seq_length
        A_       :					Dict        =      is_training
        A_       :					List[str]        =      use_input_mask
        A_       :					int        =      use_token_type_ids
        A_       :					Any        =      use_labels
        A_       :					Optional[Any]        =      vocab_size
        A_       :					List[Any]        =      hidden_size
        A_       :					str        =      num_hidden_layers
        A_       :					Optional[int]        =      num_attention_heads
        A_       :					int        =      intermediate_size
        A_       :					Optional[Any]        =      hidden_act
        A_       :					Optional[Any]        =      hidden_dropout_prob
        A_       :					Union[str, Any]        =      attention_probs_dropout_prob
        A_       :					Dict        =      max_position_embeddings
        A_       :					str        =      type_vocab_size
        A_       :					List[Any]        =      type_sequence_label_size
        A_       :					Tuple        =      initializer_range
        A_       :					Any        =      num_labels
        A_       :					int        =      num_choices
        A_       :					List[str]        =      scope



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					str        =      ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size	)

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

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

        A_       :					Optional[Any]        =      self.get_config()

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



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        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     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Tuple      ,   snake_case							:List[Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:Optional[Any]      ,   snake_case							:str      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      DistilBertModel(config=snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					Union[str, Any]        =      model(snake_case      ,   snake_case	)
        A_       :					Optional[Any]        =      model(snake_case	)
        self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:Optional[Any]      ,   snake_case							:Optional[int]      ,   snake_case							:Optional[Any]      ,   snake_case							:List[Any]      ,   snake_case							:List[str]      ,   snake_case							:Any	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      DistilBertForMaskedLM(config=snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					List[Any]        =      model(snake_case      ,   attention_mask=snake_case      ,   labels=snake_case	)
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Optional[int]      ,   snake_case							:List[Any]      ,   snake_case							:Any      ,   snake_case							:Tuple      ,   snake_case							:Any      ,   snake_case							:List[str]	):





        '''simple docstring'''




        A_       :					Any        =      DistilBertForQuestionAnswering(config=snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					Union[str, Any]        =      model(
            snake_case      ,   attention_mask=snake_case      ,   start_positions=snake_case      ,   end_positions=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     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:List[str]      ,   snake_case							:List[Any]      ,   snake_case							:List[Any]      ,   snake_case							:int      ,   snake_case							:Any      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Dict        =      self.num_labels
        A_       :					str        =      DistilBertForSequenceClassification(snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					Any        =      model(snake_case      ,   attention_mask=snake_case      ,   labels=snake_case	)
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:Dict      ,   snake_case							:int      ,   snake_case							:List[str]      ,   snake_case							:str      ,   snake_case							:List[str]      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					int        =      self.num_labels
        A_       :					Optional[int]        =      DistilBertForTokenClassification(config=snake_case	)
        model.to(snake_case	)
        model.eval()

        A_       :					int        =      model(snake_case      ,   attention_mask=snake_case      ,   labels=snake_case	)
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels)	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]      ,   snake_case							:Any      ,   snake_case							:Union[str, Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:List[Any]      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.num_choices
        A_       :					Optional[Any]        =      DistilBertForMultipleChoice(config=snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					str        =      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_       :					Tuple        =      model(
            snake_case      ,   attention_mask=snake_case      ,   labels=snake_case      ,   )
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices)	)





     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Dict        =      self.prepare_config_and_inputs()
        ((A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_))       :					Union[str, Any]        =      config_and_inputs
        A_       :					List[Any]        =      {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_torch
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  (
         (
             DistilBertModel,
             DistilBertForMaskedLM,
             DistilBertForMultipleChoice,
             DistilBertForQuestionAnswering,
             DistilBertForSequenceClassification,
             DistilBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     __UpperCamelCase            =  (
         {
             '''feature-extraction''': DistilBertModel,
             '''fill-mask''': DistilBertForMaskedLM,
             '''question-answering''': DistilBertForQuestionAnswering,
             '''text-classification''': DistilBertForSequenceClassification,
             '''token-classification''': DistilBertForTokenClassification,
             '''zero-shot''': DistilBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase            =  True
     __UpperCamelCase            =  True
     __UpperCamelCase            =  True
     __UpperCamelCase            =  True



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					Any        =      DistilBertModelTester(self	)
        A_       :					Tuple        =      ConfigTester(self      ,   config_class=snake_case      ,   dim=37	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        self.config_tester.run_common_tests()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					List[str]        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*snake_case	)



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





        '''simple docstring'''




        A_       :					Tuple        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Any        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					List[Any]        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					int        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*snake_case	)



     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_       :					Optional[Any]        =      DistilBertModel.from_pretrained(snake_case	)
           self.assertIsNotNone(snake_case	)





     @slow
     @require_torch_gpu
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_   ,    A_       :					int        =      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

           A_       :					int        =      True
           A_       :					Optional[Any]        =      model_class(config=snake_case	)

           A_       :					Union[str, Any]        =      self._prepare_for_class(snake_case      ,   snake_case	)
           A_       :					Any        =      torch.jit.trace(
               snake_case      ,   (inputs_dict["input_ids"].to("cpu"	), inputs_dict["attention_mask"].to("cpu"	))	)

           with tempfile.TemporaryDirectory() as tmp:
              torch.jit.save(snake_case      ,   os.path.join(snake_case      ,   "traced_model.pt"	)	)
              A_       :					Dict        =      torch.jit.load(os.path.join(snake_case      ,   "traced_model.pt"	)      ,   map_location=snake_case	)
              loaded(inputs_dict["input_ids"].to(snake_case	)      ,   inputs_dict["attention_mask"].to(snake_case	)	)




@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Tuple        =      DistilBertModel.from_pretrained("distilbert-base-uncased"	)
        A_       :					List[str]        =      torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]	)
        A_       :					Tuple        =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
        with torch.no_grad():
           A_       :					Optional[Any]        =      model(snake_case      ,   attention_mask=snake_case	)[0]
        A_       :					Optional[Any]        =      torch.Size((1, 11, 768)	)
        self.assertEqual(output.shape      ,   snake_case	)
        A_       :					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]      ,   snake_case      ,   atol=1e-4	)	)







import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      (0, 0)
        A_       :					Dict        =      None
        A_       :					Optional[Any]        =      0
        A_       :					Optional[Any]        =      0
        A_       :					List[Any]        =      0



     def __eq__(				self							:int      ,   snake_case							:str	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Optional[Any]=(5, 5)	):





        '''simple docstring'''




        A_       :					Optional[int]        =      np.zeros(snake_case	)
        A_       :					Tuple        =      world_size[0]
        A_       :					Optional[Any]        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A_       :					Union[str, Any]        =      cell.position[0]
        A_       :					int        =      cell.position[1]
        A_       :					Optional[int]        =      []
        for n in neughbour_cord:
           A_       :					List[str]        =      current_x + n[0]
           A_       :					Optional[Any]        =      current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
              A_       :					List[Any]        =      Cell()
              A_       :					Tuple        =      (x, y)
              A_       :					List[Any]        =      cell
              neighbours.append(snake_case	)
        return neighbours




def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			str						)			->    str:
   A_       :					Optional[Any]        =      []
   A_       :					Tuple        =      []
   _open.append(_lowerCAmelCase						)

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

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


if __name__ == "__main__":
 _lowerCAmelCase				:   Union[str, Any]						       =						Gridworld()
 # Start position and goal
 _lowerCAmelCase				:   Tuple						       =						Cell()
 _lowerCAmelCase				:   str						       =						(0, 0)
 _lowerCAmelCase				:   Union[str, Any]						       =						Cell()
 _lowerCAmelCase				:   List[str]						       =						(4, 4)
 print(F'''path from {start.position} to {goal.position}''')
 _lowerCAmelCase				:   Union[str, Any]						       =						astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  _lowerCAmelCase				:   Any						       =						1
 print(world.w)
