




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
     CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     CTRLForSequenceClassification,
     CTRLLMHeadModel,
     CTRLModel,
 )

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: Any ,       snake_case					: str=14 ,       snake_case					: Dict=7 ,       snake_case					: Any=True ,       snake_case					: Any=True ,       snake_case					: str=True ,       snake_case					: List[str]=True ,       snake_case					: int=True ,       snake_case					: List[Any]=99 ,       snake_case					: Optional[int]=32 ,       snake_case					: str=5 ,       snake_case					: int=4 ,       snake_case					: str=37 ,       snake_case					: Union[str, Any]="gelu" ,       snake_case					: List[str]=0.1 ,       snake_case					: Optional[int]=0.1 ,       snake_case					: Tuple=512 ,       snake_case					: int=16 ,       snake_case					: Any=2 ,       snake_case					: List[str]=0.02 ,       snake_case					: List[Any]=3 ,       snake_case					: str=4 ,       snake_case					: Tuple=None ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						seq_length
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_token_type_ids
          __UpperCamelCase  		=						use_input_mask
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						use_mc_token_ids
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						num_labels
          __UpperCamelCase  		=						num_choices
          __UpperCamelCase  		=						scope
          __UpperCamelCase  		=						self.vocab_size - 1







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

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

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

          __UpperCamelCase  		=						None
          if self.use_mc_token_ids:
              __UpperCamelCase  		=						ids_tensor([self.batch_size, self.num_choices] ,       self.seq_length				)

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

          __UpperCamelCase  		=						self.get_config()

          __UpperCamelCase  		=						ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,       2				)

          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )







      def       snake_case	(     self					: Optional[int]				):
          return CTRLConfig(
              vocab_size=self.vocab_size ,       n_embd=self.hidden_size ,       n_layer=self.num_hidden_layers ,       n_head=self.num_attention_heads ,       n_positions=self.max_position_embeddings ,       pad_token_id=self.pad_token_id ,       )







      def       snake_case	(     self					: Any ,       snake_case					: Optional[int] ,       snake_case					: Any ,       snake_case					: Union[str, Any] ,       snake_case					: Dict ,       snake_case					: List[Any] ,       *snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						CTRLModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()

          model(snake_case ,       token_type_ids=snake_case ,       head_mask=snake_case				)
          model(snake_case ,       token_type_ids=snake_case				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(len(result.past_key_values				) ,       config.n_layer				)







      def       snake_case	(     self					: Any ,       snake_case					: Tuple ,       snake_case					: Tuple ,       snake_case					: Union[str, Any] ,       snake_case					: Dict ,       snake_case					: List[str] ,       *snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						CTRLLMHeadModel(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						model(snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.loss.shape ,       ()				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()

          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						config_and_inputs

          __UpperCamelCase  		=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

          return config, inputs_dict







      def       snake_case	(     self					: List[Any] ,       snake_case					: int ,       snake_case					: Dict ,       snake_case					: Optional[Any] ,       snake_case					: Any ,       *snake_case					: str				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						CTRLForSequenceClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size				)
          __UpperCamelCase  		=						model(snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)				)







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
      lowerCAmelCase__			: List[str]    =					(CTRLLMHeadModel,) if is_torch_available() else ()
      lowerCAmelCase__			: str    =					(
          {
              "feature-extraction": CTRLModel,
              "text-classification": CTRLForSequenceClassification,
              "text-generation": CTRLLMHeadModel,
              "zero-shot": CTRLForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: Dict    =					True
      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: str    =					False







      def       snake_case	(     self					: str ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: Dict ,       snake_case					: List[str]				):
          if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
              # config could not be created.
              return True

          return False







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						CTRLModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       n_embd=37				)







      def       snake_case	(     self					: Union[str, Any]				):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          torch.cuda.empty_cache()







      def       snake_case	(     self					: Dict				):
          self.config_tester.run_common_tests()







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_model(*snake_case				)







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*snake_case				)







      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
      def       snake_case	(     self					: List[Any]				):
          pass







      @slow
      def       snake_case	(     self					: Optional[Any]				):
          for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						CTRLModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







      @unittest.skip('''The model doesn\'t support left padding'''				)  # and it's not used enough to be worth fixing :)
      def       snake_case	(     self					: List[Any]				):
          pass







@require_torch
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      def       snake_case	(     self					: int				):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          torch.cuda.empty_cache()







      @slow
      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						CTRLLMHeadModel.from_pretrained('''ctrl'''				)
          model.to(snake_case				)
          __UpperCamelCase  		=						torch.tensor(
              [[11859, 0, 1611, 8]] ,       dtype=torch.long ,       device=snake_case				)  # Legal the president is
          __UpperCamelCase  		=						[
              11859,
              0,
              1611,
              8,
              5,
              150,
              26449,
              2,
              19,
              348,
              469,
              3,
              2595,
              48,
              20740,
              246533,
              246533,
              19,
              30,
              5,
          ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

          __UpperCamelCase  		=						model.generate(snake_case ,       do_sample=snake_case				)
          self.assertListEqual(output_ids[0].tolist() ,       snake_case				)












import heapq
import sys

import numpy as np

a_   		=     tuple[int, int]

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Dict				):
          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						set()







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







      def       snake_case	(     self					: Optional[Any]				):
          return len(self.elements				) == 0







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: Any				):
          if item not in self.set:
              heapq.heappush(self.elements ,       (priority, item)				)
              self.set.add(snake_case				)
          else:
              # update
              # print("update", item)
              __UpperCamelCase  		=						[]
              ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              while x != item:
                  temp.append((pri, x)				)
                  ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              temp.append((priority, item)				)
              for pro, xxx in temp:
                  heapq.heappush(self.elements ,       (pro, xxx)				)







      def       snake_case	(     self					: Any ,       snake_case					: int				):
          if item in self.set:
              self.set.remove(snake_case				)
              __UpperCamelCase  		=						[]
              ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              while x != item:
                  temp.append((pro, x)				)
                  ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
              for prito, yyy in temp:
                  heapq.heappush(self.elements ,       (prito, yyy)				)







      def       snake_case	(     self					: Tuple				):
          return self.elements[0][1]







      def       snake_case	(     self					: List[str]				):
          ((__UpperCamelCase)		,      (__UpperCamelCase))  		=						heapq.heappop(self.elements				)
          self.set.remove(snake_case				)
          return (priority, item)







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						np.array(lowercase_      )
    __UpperCamelCase  		=						np.array(lowercase_      )
    return np.linalg.norm(a - b      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    return consistent_heuristic(lowercase_					,     lowercase_      ) // t




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						g_function[start] + Wa * heuristics[i](lowercase_					,     lowercase_      )
    return ans




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


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

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

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

    for i in range(lowercase_      ):
        for j in range(lowercase_      ):
            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:-'''      )
    __UpperCamelCase  		=						back_pointer[goal]
    while x != start:
        print(lowercase_					,     end=''' '''      )
        __UpperCamelCase  		=						back_pointer[x]
    print(lowercase_      )
    sys.exit()




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	List[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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	Tuple:

    """simple docstring"""


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

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

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




def 					__SCREAMING_SNAKE_CASE   (  )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    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_   		=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a_   		=     [
    (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_   		=     make_common_ground()


a_   		=     blocks_blk
# hyper parameters
a_   		=     1
a_   		=     1
a_   		=     20
a_   		=     3  # one consistent and two other inconsistent

# start and end destination
a_   		=     (0, 0)
a_   		=     (n - 1, n - 1)

a_   		=     1




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


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

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

    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						[]
    while open_list[0].minkey() < float('''inf'''      ):
        for i in range(1					,     lowercase_      ):
            # 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(lowercase_					,     lowercase_					,     lowercase_      )
                else:
                    __UpperCamelCase		,      __UpperCamelCase  		=						open_list[i].top_show()
                    visited.add(lowercase_      )
                    expand_state(
                        lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )
                    close_list_inad.append(lowercase_      )
            else:
                if g_function[goal] <= open_list[0].minkey():
                    if g_function[goal] < float('''inf'''      ):
                        do_something(lowercase_					,     lowercase_					,     lowercase_      )
                else:
                    __UpperCamelCase  		=						open_list[0].top_show()
                    visited.add(lowercase_      )
                    expand_state(
                        lowercase_					,     0					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )
                    close_list_anchor.append(lowercase_      )
    print('''No path found to goal'''      )
    print()
    for i in range(n - 1					,     -1					,     -1      ):
        for j in range(lowercase_      ):
            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)






