


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__snake_case   							=						random.Random()

if is_torch_available():
    import torch






def 		_A		(		_lowercase					,  _lowercase=1.0					,  _lowercase=None					,  _lowercase=None )					-> Dict:


     """simple docstring"""


     if rng is None:
          __UpperCamelCase =			global_rng

     __UpperCamelCase =			[]
     for batch_idx in range(shape[0] ):
          values.append([] )
          for _ in range(shape[1] ):
               values[-1].append(rng.random() * scale )

     return values



class 	__lowerCamelCase			(unittest.TestCase						):







       def __init__(			self: List[Any],A_: int,A_: Optional[int]=7,A_: Tuple=400,A_: Optional[int]=2000,A_: str=1,A_: Dict=0.0,A_: Any=1_6000,A_: List[Any]=True,A_: List[Any]=True,):






            '''simple docstring'''




            __UpperCamelCase =			parent
            __UpperCamelCase =			batch_size
            __UpperCamelCase =			min_seq_length
            __UpperCamelCase =			max_seq_length
            __UpperCamelCase =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            __UpperCamelCase =			feature_size
            __UpperCamelCase =			padding_value
            __UpperCamelCase =			sampling_rate
            __UpperCamelCase =			return_attention_mask
            __UpperCamelCase =			do_normalize







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "return_attention_mask": self.return_attention_mask,
                "do_normalize": self.do_normalize,
            }







       def 		snake_case_	(			self: Any,A_: Tuple=False,A_: int=False					):






            '''simple docstring'''




            def _flatten(A_: Optional[int]					):
                 return list(itertools.chain(*A_					)					)

            if equal_length:
                 __UpperCamelCase =			floats_list((self.batch_size, self.max_seq_length)					)
            else:
                 # make sure that inputs increase in size
                 __UpperCamelCase =			[
                     _flatten(floats_list((x, self.feature_size)					)					)
                     for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff					)
                 ]

            if numpify:
                 __UpperCamelCase =			[np.asarray(A_					) for x in speech_inputs]

            return speech_inputs



@require_torch
@require_torchaudio
class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     ASTFeatureExtractor







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			ASTFeatureExtractionTester(self					)







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            __UpperCamelCase =			[floats_list((1, x)					)[0] for x in range(800,1400,200					)]
            __UpperCamelCase =			[np.asarray(A_					) for speech_input in speech_inputs]

            # Test not batched input
            __UpperCamelCase =			feat_extract(speech_inputs[0],return_tensors='np'					).input_values
            __UpperCamelCase =			feat_extract(np_speech_inputs[0],return_tensors='np'					).input_values
            self.assertTrue(np.allclose(A_,A_,atol=1E-3					)					)

            # Test batched
            __UpperCamelCase =			feat_extract(A_,padding=A_,return_tensors='np'					).input_values
            __UpperCamelCase =			feat_extract(A_,padding=A_,return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(A_,A_					):
                 self.assertTrue(np.allclose(A_,A_,atol=1E-3					)					)

            # Test 2-D numpy arrays are batched.
            __UpperCamelCase =			[floats_list((1, x)					)[0] for x in (800, 800, 800)]
            __UpperCamelCase =			np.asarray(A_					)
            __UpperCamelCase =			feat_extract(A_,return_tensors='np'					).input_values
            __UpperCamelCase =			feat_extract(A_,return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(A_,A_					):
                 self.assertTrue(np.allclose(A_,A_,atol=1E-3					)					)







       @require_torch
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            import torch

            __UpperCamelCase =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            __UpperCamelCase =			np.random.rand(100					).astype(np.floataa					)
            __UpperCamelCase =			np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                 __UpperCamelCase =			feature_extractor.pad([{'input_values': inputs}],return_tensors='np'					)
                 self.assertTrue(np_processed.input_values.dtype == np.floataa					)
                 __UpperCamelCase =			feature_extractor.pad([{'input_values': inputs}],return_tensors='pt'					)
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)







       def 		snake_case_	(			self: Any,A_: Union[str, Any]					):






            '''simple docstring'''




            from datasets import load_dataset

            __UpperCamelCase =			load_dataset('hf-internal-testing/librispeech_asr_dummy','clean',split='validation'					)
            # automatic decoding with librispeech
            __UpperCamelCase =			ds.sort('id'					).select(range(A_					)					)[:num_samples]['audio']

            return [x["array"] for x in speech_samples]







       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			torch.tensor(
                [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
                 -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
                 -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
                 -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]					)
            # fmt: on

            __UpperCamelCase =			self._load_datasamples(1					)
            __UpperCamelCase =			ASTFeatureExtractor()
            __UpperCamelCase =			feature_extractor(A_,return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape,(1, 1024, 128)					)
            self.assertTrue(torch.allclose(input_values[0, 0, :30],A_,atol=1E-4					)					)






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__snake_case   							=						0

__snake_case   							=						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__snake_case   							=						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__snake_case   							=						tuple[int, int]



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: int,A_: int,A_: int,A_: int,A_: int,A_: Node | None,):






            '''simple docstring'''




            __UpperCamelCase =			pos_x
            __UpperCamelCase =			pos_y
            __UpperCamelCase =			(pos_y, pos_x)
            __UpperCamelCase =			goal_x
            __UpperCamelCase =			goal_y
            __UpperCamelCase =			g_cost
            __UpperCamelCase =			parent
            __UpperCamelCase =			self.calculate_heuristic()
            __UpperCamelCase =			self.g_cost + self.h_cost







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.pos_x - self.goal_x
            __UpperCamelCase =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(A_					) + abs(A_					)
            else:
                 return sqrt(dy**2 + dx**2					)







       def __lt__(			self: int,A_: Node					):






            '''simple docstring'''




            return self.f_cost < other.f_cost



class 	__lowerCamelCase			:







       def __init__(			self: Any,A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			Node(start[1],start[0],goal[1],goal[0],0,A_					)
            __UpperCamelCase =			Node(goal[1],goal[0],goal[1],goal[0],9_9999,A_					)

            __UpperCamelCase =			[self.start]
            __UpperCamelCase =			[]

            __UpperCamelCase =			False







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 __UpperCamelCase =			self.open_nodes.pop(0					)

                 if current_node.pos == self.target.pos:
                      return self.retrace_path(A_					)

                 self.closed_nodes.append(A_					)
                 __UpperCamelCase =			self.get_successors(A_					)

                 for child_node in successors:
                      if child_node in self.closed_nodes:
                           continue

                      if child_node not in self.open_nodes:
                           self.open_nodes.append(A_					)
                      else:
                           # retrieve the best current path
                           __UpperCamelCase =			self.open_nodes.pop(self.open_nodes.index(A_					)					)

                           if child_node.g_cost < better_node.g_cost:
                                self.open_nodes.append(A_					)
                           else:
                                self.open_nodes.append(A_					)

            return [self.start.pos]







       def 		snake_case_	(			self: int,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            for action in delta:
                 __UpperCamelCase =			parent.pos_x + action[1]
                 __UpperCamelCase =			parent.pos_y + action[0]
                 if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(A_					) - 1):
                      continue

                 if grid[pos_y][pos_x] != 0:
                      continue

                 successors.append(
                     Node(
                         A_,A_,self.target.pos_y,self.target.pos_x,parent.g_cost + 1,A_,)					)
            return successors







       def 		snake_case_	(			self: Any,A_: Node | None					):






            '''simple docstring'''




            __UpperCamelCase =			node
            __UpperCamelCase =			[]
            while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)					)
                 __UpperCamelCase =			current_node.parent
            path.reverse()
            return path



class 	__lowerCamelCase			:







       def __init__(			self: List[Any],A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			False







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






            '''simple docstring'''




            while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 __UpperCamelCase =			self.fwd_astar.open_nodes.pop(0					)
                 __UpperCamelCase =			self.bwd_astar.open_nodes.pop(0					)

                 if current_bwd_node.pos == current_fwd_node.pos:
                      return self.retrace_bidirectional_path(
                          A_,A_					)

                 self.fwd_astar.closed_nodes.append(A_					)
                 self.bwd_astar.closed_nodes.append(A_					)

                 __UpperCamelCase =			current_bwd_node
                 __UpperCamelCase =			current_fwd_node

                 __UpperCamelCase =			{
                     self.fwd_astar: self.fwd_astar.get_successors(A_					),
                     self.bwd_astar: self.bwd_astar.get_successors(A_					),
                 }

                 for astar in [self.fwd_astar, self.bwd_astar]:
                      for child_node in successors[astar]:
                           if child_node in astar.closed_nodes:
                                continue

                           if child_node not in astar.open_nodes:
                                astar.open_nodes.append(A_					)
                           else:
                                # retrieve the best current path
                                __UpperCamelCase =			astar.open_nodes.pop(
                                    astar.open_nodes.index(A_					)					)

                                if child_node.g_cost < better_node.g_cost:
                                     astar.open_nodes.append(A_					)
                                else:
                                     astar.open_nodes.append(A_					)

            return [self.fwd_astar.start.pos]







       def 		snake_case_	(			self: List[str],A_: Node,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			self.fwd_astar.retrace_path(A_					)
            __UpperCamelCase =			self.bwd_astar.retrace_path(A_					)
            bwd_path.pop()
            bwd_path.reverse()
            __UpperCamelCase =			fwd_path + bwd_path
            return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    __snake_case   							=						(0, 0)
    __snake_case   							=						(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    __snake_case   							=						time.time()
    __snake_case   							=						AStar(init, goal)
    __snake_case   							=						a_star.search()
    __snake_case   							=						time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __snake_case   							=						time.time()
    __snake_case   							=						BidirectionalAStar(init, goal)
    __snake_case   							=						time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")


