


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

_snake_case  :	Union[str, Any]			      =       random.Random()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=1.0							,			__lowerCamelCase=None							,			__lowerCamelCase=None			):
							if rng is None:
														__snake_case	:    str											=     global_rng

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

							return values

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def __init__(							self :						List[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[Any]=7					,			lowerCamelCase :						Union[str, Any]=400					,			lowerCamelCase :						List[Any]=2000					,			lowerCamelCase :						Optional[int]=2048					,			lowerCamelCase :						Any=128					,			lowerCamelCase :						int=1					,			lowerCamelCase :						Optional[Any]=512					,			lowerCamelCase :						Tuple=30					,			lowerCamelCase :						Optional[int]=44100					,			)			->					str:
														__snake_case	:    List[str]											=     parent
														__snake_case	:    int											=     batch_size
														__snake_case	:    int											=     min_seq_length
														__snake_case	:    Dict											=     max_seq_length
														__snake_case	:    Optional[int]											=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														__snake_case	:    Any											=     spectrogram_length
														__snake_case	:    Dict											=     feature_size
														__snake_case	:    Dict											=     num_audio_channels
														__snake_case	:    int											=     hop_length
														__snake_case	:    int											=     chunk_length
														__snake_case	:    List[str]											=     sampling_rate

							def 					__snake_case      (							self :						Any       )			->					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 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						int=False					,			lowerCamelCase :						List[str]=False       )			->					Optional[int]:
														def _flatten(lowerCamelCase :						List[Any]       ):
																					return list(itertools.chain(*lowerCamelCase       )       )

														if equal_length:
																					__snake_case	:    Dict											=     [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	:    List[str]											=     [
																					    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	:    Optional[Any]											=     [np.asarray(lowerCamelCase       ) for x in speech_inputs]
														return speech_inputs

@require_torch
@require_torchaudio
class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[int]  						=						TvltFeatureExtractor

							def 					__snake_case      (							self :						Optional[Any]       )			->					List[Any]:
														__snake_case	:    List[str]											=     TvltFeatureExtractionTester(self       )

							def 					__snake_case      (							self :						Optional[int]       )			->					int:
														__snake_case	:    str											=     self.feature_extraction_class(**self.feat_extract_dict       )
														self.assertTrue(hasattr(lowerCamelCase					,			"spectrogram_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"feature_size"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"num_audio_channels"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"hop_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"chunk_length"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"sampling_rate"       )       )

							def 					__snake_case      (							self :						Any       )			->					Any:
														__snake_case	:    Union[str, Any]											=     self.feature_extraction_class(**self.feat_extract_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					__snake_case	:    Optional[int]											=     feat_extract_first.save_pretrained(lowerCamelCase       )[0]
																					check_json_file_has_correct_format(lowerCamelCase       )
																					__snake_case	:    Any											=     self.feature_extraction_class.from_pretrained(lowerCamelCase       )

														__snake_case	:    str											=     feat_extract_first.to_dict()
														__snake_case	:    Optional[Any]											=     feat_extract_second.to_dict()
														__snake_case	:    Optional[Any]											=     dict_first.pop("mel_filters"       )
														__snake_case	:    int											=     dict_second.pop("mel_filters"       )
														self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase       )       )
														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Dict       )			->					List[Any]:
														__snake_case	:    int											=     self.feature_extraction_class(**self.feat_extract_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					__snake_case	:    Dict											=     os.path.join(lowerCamelCase					,			"feat_extract.json"       )
																					feat_extract_first.to_json_file(lowerCamelCase       )
																					__snake_case	:    Optional[Any]											=     self.feature_extraction_class.from_json_file(lowerCamelCase       )

														__snake_case	:    int											=     feat_extract_first.to_dict()
														__snake_case	:    Optional[Any]											=     feat_extract_second.to_dict()
														__snake_case	:    Optional[Any]											=     dict_first.pop("mel_filters"       )
														__snake_case	:    Optional[int]											=     dict_second.pop("mel_filters"       )
														self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase       )       )
														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						List[Any]       )			->					Optional[int]:
														# Initialize feature_extractor
														__snake_case	:    Dict											=     self.feature_extraction_class(**self.feat_extract_dict       )

														# create three inputs of length 800, 1000, and 1200
														__snake_case	:    Union[str, Any]											=     [floats_list((1, x)       )[0] for x in range(800					,			1400					,			200       )]
														__snake_case	:    Optional[int]											=     [np.asarray(lowerCamelCase       ) for speech_input in speech_inputs]

														# Test not batched input
														__snake_case	:    Union[str, Any]											=     feature_extractor(np_speech_inputs[0]					,			return_tensors="np"					,			sampling_rate=44100       ).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	:    Tuple											=     feature_extractor(lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100       ).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	:    List[Any]											=     feature_extractor(
														    lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100					,			mask_audio=lowerCamelCase       ).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	:    List[str]											=     [floats_list((1, x)       )[0] for x in (800, 800, 800)]
														__snake_case	:    List[str]											=     np.asarray(lowerCamelCase       )
														__snake_case	:    Tuple											=     feature_extractor(lowerCamelCase					,			return_tensors="np"					,			sampling_rate=44100       ).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 					__snake_case      (							self :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     load_dataset("hf-internal-testing/librispeech_asr_dummy"					,			"clean"					,			split="validation"       )
														# automatic decoding with librispeech
														__snake_case	:    Union[str, Any]											=     ds.sort("id"       ).select(range(lowerCamelCase       )       )[:num_samples]["audio"]

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






							def 					__snake_case      (							self :						Tuple       )			->					Tuple:
														__snake_case	:    Any											=     self._load_datasamples(1       )
														__snake_case	:    Any											=     TvltFeatureExtractor()
														__snake_case	:    str											=     feature_extractor(lowerCamelCase					,			return_tensors="pt"       ).audio_values

														self.assertEquals(audio_values.shape					,			(1, 1, 192, 128)       )

														__snake_case	:    Dict											=     torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]       )
														self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,			lowerCamelCase					,			atol=1E-4       )       )





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

