
"""simple docstring"""

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


_A						=			random.Random()

if is_torch_available():
					import torch
def 		UpperCAmelCase (		a_,						a_=1.0,						a_=None,						a_=None      ):





		'''simple docstring'''


		if rng is None:
				lowerCamelCase					:      Any          =   global_rng

		lowerCamelCase					:      Union[str, Any]          =   []
		for batch_idx in range(shape[0]      ):
				values.append([]      )
				for _ in range(shape[1]      ):
						values[-1].append(rng.random() * scale      )

		return values






class 	_lowercase       (    unittest.TestCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=7		,			UpperCAmelCase_=400		,			UpperCAmelCase_=2000		,			UpperCAmelCase_=1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=16000		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			)		->							List[Any]:
			lowerCamelCase					:      List[str]          =   parent
			lowerCamelCase					:      int          =   batch_size
			lowerCamelCase					:      List[str]          =   min_seq_length
			lowerCamelCase					:      List[Any]          =   max_seq_length
			lowerCamelCase					:      List[str]          =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCamelCase					:      str          =   feature_size
			lowerCamelCase					:      List[Any]          =   padding_value
			lowerCamelCase					:      Tuple          =   sampling_rate
			lowerCamelCase					:      Dict          =   return_attention_mask
			lowerCamelCase					:      Optional[Any]          =   do_normalize




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			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       _UpperCamelCase   (			self		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False )		->							str:
			def _flatten(UpperCAmelCase_ ):
					return list(itertools.chain(*UpperCAmelCase_ ) )

			if equal_length:
					lowerCamelCase					:      Optional[int]          =   floats_list((self.batch_size, self.max_seq_length) )
			else:
					# make sure that inputs increase in size
					lowerCamelCase					:      List[str]          =   [
					    _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:
					lowerCamelCase					:      List[str]          =   [np.asarray(UpperCAmelCase_ ) for x in speech_inputs]

			return speech_inputs






@require_torch
@require_torchaudio
class 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     ASTFeatureExtractor




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Optional[Any]          =   ASTFeatureExtractionTester(self )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCamelCase					:      Union[str, Any]          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			# create three inputs of length 800, 1000, and 1200
			lowerCamelCase					:      Union[str, Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
			lowerCamelCase					:      List[Any]          =   [np.asarray(UpperCAmelCase_ ) for speech_input in speech_inputs]

			# Test not batched input
			lowerCamelCase					:      List[str]          =   feat_extract(speech_inputs[0]		,			return_tensors='np' ).input_values
			lowerCamelCase					:      Union[str, Any]          =   feat_extract(np_speech_inputs[0]		,			return_tensors='np' ).input_values
			self.assertTrue(np.allclose(UpperCAmelCase_		,			UpperCAmelCase_		,			atol=1E-3 ) )

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

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




	@require_torch
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			import torch

			lowerCamelCase					:      Any          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			lowerCamelCase					:      Optional[int]          =   np.random.rand(100 ).astype(np.floataa )
			lowerCamelCase					:      List[Any]          =   np_speech_inputs.tolist()

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
			from datasets import load_dataset

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

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




	@require_torch
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# fmt: off
			lowerCamelCase					:      str          =   torch.tensor(
			    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
			     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
			     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
			     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869] )
			# fmt: on

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








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   False  # flag set if we can't find expand

		while not found and not resign:
				if len(a_      ) == 0:
						raise ValueError('Algorithm is unable to find solution'      )
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(a_      ) and ya >= 0 and ya < len(grid[0]      ):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 1, 0],
					    [0, 0, 0, 0, 1, 0],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
					for i in range(len(grid)):
										for j in range(len(grid[0])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			search(grid, init, goal, cost, heuristic)

					print('ACTION MAP')
					for i in range(len(action)):
										print(action[i])

					for i in range(len(path)):
										print(path[i])






