






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

__lowerCamelCase      :    int   		=   random.Random()







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							str=1.0  ,						snake_case_      :							Dict=None  ,						snake_case_      :							Tuple=None     ):
	if rng is None:
		snake_case__				:   Optional[int]     =						global_rng

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

	return values

class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						int=7							,	__A  :						Any=4_0_0							,	__A  :						Tuple=2_0_0_0							,	__A  :						Tuple=2_0_4_8							,	__A  :						List[str]=1_2_8							,	__A  :						Dict=1							,	__A  :						int=5_1_2							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_4_1_0_0							,	):
						snake_case__				:   str     =						parent
						snake_case__				:   Optional[Any]     =						batch_size
						snake_case__				:   Optional[Any]     =						min_seq_length
						snake_case__				:   Optional[int]     =						max_seq_length
						snake_case__				:   List[Any]     =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						snake_case__				:   List[str]     =						spectrogram_length
						snake_case__				:   Union[str, Any]     =						feature_size
						snake_case__				:   int     =						num_audio_channels
						snake_case__				:   Tuple     =						hop_length
						snake_case__				:   str     =						chunk_length
						snake_case__				:   Optional[int]     =						sampling_rate



					def  _lowercase  (      self  :						str ):
						return {
						    "spectrogram_length": self.spectrogram_length,
						    "feature_size": self.feature_size,
						    "num_audio_channels": self.num_audio_channels,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "sampling_rate": self.sampling_rate,
						}






					def  _lowercase  (      self  :						int							,	__A  :						Tuple=False							,	__A  :						int=False ):
						def _flatten(__A  :						Union[str, Any] ):
							return list(itertools.chain(*__A ) )

						if equal_length:
							snake_case__				:   Any     =						[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
						else:
							# make sure that inputs increase in size
							snake_case__				:   Optional[Any]     =						[
							    floats_list((x, self.feature_size) )
							    for x in range(self.min_seq_length							,	self.max_seq_length							,	self.seq_length_diff )
							]
						if numpify:
							snake_case__				:   List[str]     =						[np.asarray(__A ) for x in speech_inputs]
						return speech_inputs

@require_torch
@require_torchaudio
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	TvltFeatureExtractor



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						TvltFeatureExtractionTester(self )



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Tuple     =						self.feature_extraction_class(**self.feat_extract_dict )
						self.assertTrue(hasattr(__A							,	"spectrogram_length" ) )
						self.assertTrue(hasattr(__A							,	"feature_size" ) )
						self.assertTrue(hasattr(__A							,	"num_audio_channels" ) )
						self.assertTrue(hasattr(__A							,	"hop_length" ) )
						self.assertTrue(hasattr(__A							,	"chunk_length" ) )
						self.assertTrue(hasattr(__A							,	"sampling_rate" ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   int     =						self.feature_extraction_class(**self.feat_extract_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   Optional[Any]     =						feat_extract_first.save_pretrained(__A )[0]
							check_json_file_has_correct_format(__A )
							snake_case__				:   List[Any]     =						self.feature_extraction_class.from_pretrained(__A )

						snake_case__				:   List[str]     =						feat_extract_first.to_dict()
						snake_case__				:   str     =						feat_extract_second.to_dict()
						snake_case__				:   List[Any]     =						dict_first.pop("mel_filters" )
						snake_case__				:   Any     =						dict_second.pop("mel_filters" )
						self.assertTrue(np.allclose(__A							,	__A ) )
						self.assertEqual(__A							,	__A )



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Dict     =						self.feature_extraction_class(**self.feat_extract_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   List[str]     =						os.path.join(__A							,	"feat_extract.json" )
							feat_extract_first.to_json_file(__A )
							snake_case__				:   Any     =						self.feature_extraction_class.from_json_file(__A )

						snake_case__				:   Dict     =						feat_extract_first.to_dict()
						snake_case__				:   Any     =						feat_extract_second.to_dict()
						snake_case__				:   List[str]     =						dict_first.pop("mel_filters" )
						snake_case__				:   int     =						dict_second.pop("mel_filters" )
						self.assertTrue(np.allclose(__A							,	__A ) )
						self.assertEqual(__A							,	__A )



					def  _lowercase  (      self  :						List[str] ):
						# Initialize feature_extractor
						snake_case__				:   Union[str, Any]     =						self.feature_extraction_class(**self.feat_extract_dict )

						# create three inputs of length 800, 1000, and 1200
						snake_case__				:   Dict     =						[floats_list((1, x) )[0] for x in range(8_0_0							,	1_4_0_0							,	2_0_0 )]
						snake_case__				:   Union[str, Any]     =						[np.asarray(__A ) for speech_input in speech_inputs]

						# Test not batched input
						snake_case__				:   Optional[int]     =						feature_extractor(np_speech_inputs[0]							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).audio_values

						self.assertTrue(encoded_audios.ndim == 4 )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

						# Test batched
						snake_case__				:   Dict     =						feature_extractor(__A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).audio_values

						self.assertTrue(encoded_audios.ndim == 4 )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

						# Test audio masking
						snake_case__				:   Union[str, Any]     =						feature_extractor(
						    __A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0							,	mask_audio=__A ).audio_values

						self.assertTrue(encoded_audios.ndim == 4 )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

						# Test 2-D numpy arrays are batched.
						snake_case__				:   Any     =						[floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						snake_case__				:   str     =						np.asarray(__A )
						snake_case__				:   Tuple     =						feature_extractor(__A							,	return_tensors="np"							,	sampling_rate=4_4_1_0_0 ).audio_values
						self.assertTrue(encoded_audios.ndim == 4 )
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Dict ):
						snake_case__				:   List[Any]     =						load_dataset("hf-internal-testing/librispeech_asr_dummy"							,	"clean"							,	split="validation" )
						# automatic decoding with librispeech
						snake_case__				:   Tuple     =						ds.sort("id" ).select(range(__A ) )[:num_samples]["audio"]

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






					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Dict     =						self._load_datasamples(1 )
						snake_case__				:   Any     =						TvltFeatureExtractor()
						snake_case__				:   List[str]     =						feature_extractor(__A							,	return_tensors="pt" ).audio_values

						self.assertEquals(audio_values.shape							,	(1, 1, 1_9_2, 1_2_8) )

						snake_case__				:   Optional[int]     =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
						self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,	__A							,	atol=1e-4 ) )











from __future__ import annotations

import time

__lowerCamelCase      :    str   		=   list[tuple[int, int]]

__lowerCamelCase      :    Optional[int]   		=   [
    [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],
]

__lowerCamelCase      :    Tuple   		=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Union[str, Any]							,	__A  :						int							,	__A  :						int							,	__A  :						int							,	__A  :						int							,	__A  :						Node | None ):
						snake_case__				:   Optional[int]     =						pos_x
						snake_case__				:   Dict     =						pos_y
						snake_case__				:   int     =						(pos_y, pos_x)
						snake_case__				:   Optional[int]     =						goal_x
						snake_case__				:   Tuple     =						goal_y
						snake_case__				:   str     =						parent



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						tuple[int, int]							,	__A  :						tuple[int, int] ):
						snake_case__				:   Tuple     =						Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	__A )
						snake_case__				:   Tuple     =						Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	__A )

						snake_case__				:   int     =						[self.start]
						snake_case__				:   Union[str, Any]     =						False



					def  _lowercase  (      self  :						Dict ):
						while self.node_queue:
							snake_case__				:   Optional[Any]     =						self.node_queue.pop(0 )

							if current_node.pos == self.target.pos:
								snake_case__				:   Optional[Any]     =						True
								return self.retrace_path(__A )

							snake_case__				:   int     =						self.get_successors(__A )

							for node in successors:
								self.node_queue.append(__A )

						if not self.reached:
							return [self.start.pos]
						return None



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Node ):
						snake_case__				:   str     =						[]
						for action in delta:
							snake_case__				:   str     =						parent.pos_x + action[1]
							snake_case__				:   Union[str, Any]     =						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							,	__A ) )
						return successors






					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Node | None ):
						snake_case__				:   Tuple     =						node
						snake_case__				:   Any     =						[]
						while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x) )
							snake_case__				:   Tuple     =						current_node.parent
						path.reverse()
						return path



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Dict							,	__A  :						str							,	__A  :						int ):
						snake_case__				:   str     =						BreadthFirstSearch(__A							,	__A )
						snake_case__				:   int     =						BreadthFirstSearch(__A							,	__A )
						snake_case__				:   Tuple     =						False



					def  _lowercase  (      self  :						Optional[Any] ):
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							snake_case__				:   Any     =						self.fwd_bfs.node_queue.pop(0 )
							snake_case__				:   List[str]     =						self.bwd_bfs.node_queue.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
								snake_case__				:   List[str]     =						True
								return self.retrace_bidirectional_path(
								    __A							,	__A )

							snake_case__				:   Union[str, Any]     =						current_bwd_node
							snake_case__				:   Dict     =						current_fwd_node

							snake_case__				:   List[Any]     =						{
							    self.fwd_bfs: self.fwd_bfs.get_successors(__A ),
							    self.bwd_bfs: self.bwd_bfs.get_successors(__A ),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
								for node in successors[bfs]:
									bfs.node_queue.append(__A )

						if not self.reached:
							return [self.fwd_bfs.start.pos]
						return None






					def  _lowercase  (      self  :						Any							,	__A  :						Node							,	__A  :						Node ):
						snake_case__				:   List[str]     =						self.fwd_bfs.retrace_path(__A )
						snake_case__				:   Optional[Any]     =						self.bwd_bfs.retrace_path(__A )
						bwd_path.pop()
						bwd_path.reverse()
						snake_case__				:   List[Any]     =						fwd_path + bwd_path
						return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	import doctest

	doctest.testmod()
	__lowerCamelCase      :    str   		=   (0, 0)
	__lowerCamelCase      :    List[str]   		=   (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	__lowerCamelCase      :    Any   		=   time.time()
	__lowerCamelCase      :    Optional[Any]   		=   BreadthFirstSearch(init, goal)
	__lowerCamelCase      :    str   		=   bfs.search()
	__lowerCamelCase      :    Optional[Any]   		=   time.time() - start_bfs_time

	print("""Unidirectional BFS computation time : """, bfs_time)

	__lowerCamelCase      :    Optional[Any]   		=   time.time()
	__lowerCamelCase      :    Optional[int]   		=   BidirectionalBreadthFirstSearch(init, goal)
	__lowerCamelCase      :    str   		=   bd_bfs.search()
	__lowerCamelCase      :    Optional[Any]   		=   time.time() - start_bd_bfs_time

	print("""Bidirectional BFS computation time : """, bd_bfs_time)



