
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase  :					str													=       random.Random()




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str]						,  _lowerCamelCase :			List[Any]=1.0						,  _lowerCamelCase :			Union[str, Any]=None						,  _lowerCamelCase :			List[Any]=None)					->    Optional[Any]:






							'''simple docstring'''





							if rng is None:
														__UpperCamelCase	:   Any			   =					global_rng

							__UpperCamelCase	:   Tuple			   =					[]
							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):




	'''simple docstring'''




	def __init__(					self		:Optional[int]     ,   a		:List[str]     ,   a		:Union[str, Any]=7     ,   a		:str=4_0_0     ,   a		:Optional[int]=2_0_0_0     ,   a		:int=1     ,   a		:Tuple=0.0     ,   a		:List[str]=1_6_0_0_0     ,   a		:Any=True     ,   a		:Tuple=True     ,   )		-> Optional[Any]:
								__UpperCamelCase	:   int			   =					parent
								__UpperCamelCase	:   str			   =					batch_size
								__UpperCamelCase	:   Tuple			   =					min_seq_length
								__UpperCamelCase	:   List[Any]			   =					max_seq_length
								__UpperCamelCase	:   List[Any]			   =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCamelCase	:   List[Any]			   =					feature_size
								__UpperCamelCase	:   List[str]			   =					padding_value
								__UpperCamelCase	:   List[str]			   =					sampling_rate
								__UpperCamelCase	:   Optional[Any]			   =					return_attention_mask
								__UpperCamelCase	:   str			   =					do_normalize







	def    _lowerCamelCase				(					self		:str						)		-> Optional[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    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Tuple=False     ,   a		:Union[str, Any]=False						)		-> Optional[Any]:
								def _flatten(a		:str						):
															return list(itertools.chain(*a						)						)

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

								return speech_inputs




class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     WavaVecaFeatureExtractor







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Tuple			   =					WavaVecaFeatureExtractionTester(self						)







	def    _lowerCamelCase				(					self		:str     ,   a		:Tuple						)		-> Any:
								self.assertTrue(np.all(np.mean(a     ,   axis=0						) < 1E-3						)						)
								self.assertTrue(np.all(np.abs(np.var(a     ,   axis=0						) - 1						) < 1E-3						)						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCamelCase	:   Tuple			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								# create three inputs of length 800, 1000, and 1200
								__UpperCamelCase	:   str			   =					[floats_list((1, x)						)[0] for x in range(8_0_0     ,   1_4_0_0     ,   2_0_0						)]
								__UpperCamelCase	:   str			   =					[np.asarray(a						) for speech_input in speech_inputs]

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

								# Test batched
								__UpperCamelCase	:   List[str]			   =					feat_extract(a     ,   return_tensors="np"						).input_values
								__UpperCamelCase	:   int			   =					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						)						)

								# Test 2-D numpy arrays are batched.
								__UpperCamelCase	:   List[str]			   =					[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__UpperCamelCase	:   Optional[Any]			   =					np.asarray(a						)
								__UpperCamelCase	:   str			   =					feat_extract(a     ,   return_tensors="np"						).input_values
								__UpperCamelCase	:   List[Any]			   =					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						)						)







	def    _lowerCamelCase				(					self		:Dict						)		-> List[str]:
								__UpperCamelCase	:   Optional[Any]			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCamelCase	:   str			   =					[floats_list((1, x)						)[0] for x in range(8_0_0     ,   1_4_0_0     ,   2_0_0						)]

								__UpperCamelCase	:   int			   =					["longest", "max_length", "do_not_pad"]
								__UpperCamelCase	:   Dict			   =					[None, 1_6_0_0, None]
								for max_length, padding in zip(a     ,   a						):
															__UpperCamelCase	:   List[Any]			   =					feat_extract(a     ,   padding=a     ,   max_length=a     ,   return_tensors="np"						)
															__UpperCamelCase	:   Optional[int]			   =					processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
															self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6						)
															self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
															self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6						)
															self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[Any]			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCamelCase	:   Any			   =					range(8_0_0     ,   1_4_0_0     ,   2_0_0						)
								__UpperCamelCase	:   Tuple			   =					[floats_list((1, x)						)[0] for x in lengths]

								__UpperCamelCase	:   Union[str, Any]			   =					["longest", "max_length", "do_not_pad"]
								__UpperCamelCase	:   Any			   =					[None, 1_6_0_0, None]

								for max_length, padding in zip(a     ,   a						):
															__UpperCamelCase	:   Tuple			   =					feat_extract(a     ,   max_length=a     ,   padding=a						)
															__UpperCamelCase	:   str			   =					processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
															self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
															self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)







	def    _lowerCamelCase				(					self		:Dict						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCamelCase	:   Union[str, Any]			   =					[floats_list((1, x)						)[0] for x in range(8_0_0     ,   1_4_0_0     ,   2_0_0						)]
								__UpperCamelCase	:   List[Any]			   =					feat_extract(
								    a     ,   truncation=a     ,   max_length=1_0_0_0     ,   padding="max_length"     ,   return_tensors="np"						)
								__UpperCamelCase	:   int			   =					processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1]						)
								self._check_zero_mean_unit_variance(input_values[2]						)







	def    _lowerCamelCase				(					self		:str						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCamelCase	:   Optional[Any]			   =					[floats_list((1, x)						)[0] for x in range(8_0_0     ,   1_4_0_0     ,   2_0_0						)]
								__UpperCamelCase	:   Optional[Any]			   =					feat_extract(
								    a     ,   truncation=a     ,   max_length=1_0_0_0     ,   padding="longest"     ,   return_tensors="np"						)
								__UpperCamelCase	:   Optional[int]			   =					processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
								self._check_zero_mean_unit_variance(input_values[2]						)

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1_0_0_0)						)

								__UpperCamelCase	:   int			   =					[floats_list((1, x)						)[0] for x in range(8_0_0     ,   1_4_0_0     ,   2_0_0						)]
								__UpperCamelCase	:   Tuple			   =					feat_extract(
								    a     ,   truncation=a     ,   max_length=2_0_0_0     ,   padding="longest"     ,   return_tensors="np"						)
								__UpperCamelCase	:   Union[str, Any]			   =					processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
								self._check_zero_mean_unit_variance(input_values[2]						)

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1_2_0_0)						)







	@require_torch
	def    _lowerCamelCase				(					self		:Tuple						)		-> Optional[Any]:
								import torch

								__UpperCamelCase	:   List[Any]			   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCamelCase	:   Dict			   =					np.random.rand(1_0_0						).astype(np.floataa						)
								__UpperCamelCase	:   str			   =					np_speech_inputs.tolist()

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







	@slow
	@require_torch
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Optional[Any]:
								# this test makes sure that models that are using
								# group norm don't have their feature extractor return the
								# attention_mask
								for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
															__UpperCamelCase	:   Optional[int]			   =					WavaVecaConfig.from_pretrained(a						)
															__UpperCamelCase	:   Optional[Any]			   =					WavaVecaFeatureExtractor.from_pretrained(a						)

															# only "layer" feature extraction norm should make use of
															# attention_mask
															self.assertEqual(feat_extract.return_attention_mask     ,   config.feat_extract_norm == "layer"						)

from __future__ import annotations




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					len(_lowerCamelCase)
							# We need to create solution object to save path.
							__UpperCamelCase	:   List[str]			   =					[[0 for _ in range(_lowerCamelCase)] for _ in range(_lowerCamelCase)]
							__UpperCamelCase	:   Optional[int]			   =					run_maze(_lowerCamelCase						,  0						,  0						,  _lowerCamelCase)
							if solved:
														print("\n".join(str(_lowerCamelCase) for row in solutions))
							else:
														print("No solution exists!")
							return solved




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			int						,  _lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					len(_lowerCamelCase)
							# Final check point.
							if i == j == (size - 1):
														__UpperCamelCase	:   Optional[int]			   =					1
														return True

							__UpperCamelCase	:   List[Any]			   =					(not i < 0) and (not j < 0)  # Check lower bounds
							__UpperCamelCase	:   List[str]			   =					(i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__UpperCamelCase	:   int			   =					(not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__UpperCamelCase	:   Tuple			   =					1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,  i + 1						,  _lowerCamelCase						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  _lowerCamelCase						,  j + 1						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  i - 1						,  _lowerCamelCase						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  _lowerCamelCase						,  j - 1						,  _lowerCamelCase)
																					):
																												return True

																					__UpperCamelCase	:   Tuple			   =					0
																					return False
							return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()