

import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=1.0       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=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






@require_torch
class 		__magic_name__ (				unittest.TestCase							):






     def __init__(							self						,		_lowercase						,		_lowercase=7						,		_lowercase=400						,		_lowercase=2_000						,		_lowercase=1						,		_lowercase=0.0						,		_lowercase=24_000						,		_lowercase=True						,		)->			List[str]:
         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






     def      UpperCAmelCase_							(							self     )->			str:
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
         }






     def      UpperCAmelCase_							(							self						,		_lowercase=False						,		_lowercase=False     )->			int:
         def _flatten(_lowercase     ):
             return list(itertools.chain(*_lowercase     )     )

         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(_lowercase     ) for x in audio_inputs]

         return audio_inputs






@require_torch
class 		__magic_name__ (				snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	EncodecFeatureExtractor






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		EnCodecFeatureExtractionTester(self     )






     def      UpperCAmelCase_							(							self     )->			List[str]:
         # Tests that all call wrap to encode_plus and batch_encode_plus
         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						,		1_400						,		200     )]
         UpperCamelCase_          =		[np.asarray(_lowercase     ) for audio_input in audio_inputs]

         # Test not batched input
         UpperCamelCase_          =		feat_extract(audio_inputs[0]						,		return_tensors="np"     ).input_values
         UpperCamelCase_          =		feat_extract(np_audio_inputs[0]						,		return_tensors="np"     ).input_values
         self.assertTrue(np.allclose(_lowercase						,		_lowercase						,		atol=1e-3     )     )

         # Test batched
         UpperCamelCase_          =		feat_extract(_lowercase						,		padding=_lowercase						,		return_tensors="np"     ).input_values
         UpperCamelCase_          =		feat_extract(_lowercase						,		padding=_lowercase						,		return_tensors="np"     ).input_values
         for enc_seq_a, enc_seq_a in zip(_lowercase						,		_lowercase     ):
             self.assertTrue(np.allclose(_lowercase						,		_lowercase						,		atol=1e-3     )     )






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
         UpperCamelCase_          =		np.random.rand(100     ).astype(np.floataa     )
         UpperCamelCase_          =		np_audio_inputs.tolist()

         for inputs in [py_audio_inputs, np_audio_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      UpperCAmelCase_							(							self						,		_lowercase     )->			Any:
         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(_lowercase     )     )[:num_samples]["audio"]

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






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         # fmt: off
         UpperCamelCase_          =		torch.tensor(
             [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
              3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
              2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
              4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
              7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
              4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]     )
         # fmt: on
         UpperCamelCase_          =		self._load_datasamples(1     )
         UpperCamelCase_          =		EncodecFeatureExtractor()
         UpperCamelCase_          =		feature_extractor(_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(input_values.shape						,		(1, 1, 93_680)     )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		_lowercase						,		atol=1e-6     )     )






     def      UpperCAmelCase_							(							self     )->			int:
         # fmt: off
         UpperCamelCase_          =		torch.tensor(
             [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
              3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
              2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
              4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
              7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
              4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]     )
         # fmt: on
         UpperCamelCase_          =		self._load_datasamples(1     )
         UpperCamelCase_          =		[np.tile(input_audio[0][None]						,		reps=(2, 1)     )]
         input_audio[0][1] *= 0.5
         UpperCamelCase_          =		EncodecFeatureExtractor(feature_size=2     )
         UpperCamelCase_          =		feature_extractor(_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(input_values.shape						,		(1, 2, 93_680)     )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		_lowercase						,		atol=1e-6     )     )
         self.assertTrue(torch.allclose(input_values[0, 1, :30]						,		EXPECTED_INPUT_VALUES * 0.5						,		atol=1e-6     )     )






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		self._load_datasamples(2     )
         # would be easier if the stride was like
         UpperCamelCase_          =		EncodecFeatureExtractor(feature_size=1						,		chunk_length_s=1						,		overlap=0.01     )

         # pad and trunc raise an error ?
         with self.assertRaisesRegex(
             _lowercase						,		"^Both padding and truncation were set. Make sure you only set one.$"						,		):
             UpperCamelCase_          =		feature_extractor(
                 _lowercase						,		padding="max_length"						,		truncation=_lowercase						,		return_tensors="pt"     ).input_values

         # truncate to chunk
         UpperCamelCase_          =		feature_extractor(_lowercase						,		truncation=_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(2, 1, 71_520)     )  # 2 chunks

         # force truncate to max_length
         UpperCamelCase_          =		feature_extractor(
             _lowercase						,		truncation=_lowercase						,		max_length=48_000						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(2, 1, 48_000)     )

         # pad to chunk
         UpperCamelCase_          =		feature_extractor(_lowercase						,		padding=_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(padded_outputs.shape						,		(2, 1, 95_280)     )

         # pad to chunk
         UpperCamelCase_          =		feature_extractor(_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(2, 1, 95_280)     )

         # force pad to max length
         UpperCamelCase_          =		feature_extractor(
             _lowercase						,		padding="max_length"						,		max_length=100_000						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(2, 1, 100_000)     )

         # force no pad
         with self.assertRaisesRegex(
             _lowercase						,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"						,		):
             UpperCamelCase_          =		feature_extractor(_lowercase						,		padding=_lowercase						,		return_tensors="pt"     ).input_values

         UpperCamelCase_          =		feature_extractor(input_audio[0]						,		padding=_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(1, 1, 93_680)     )

         # no pad if no chunk_length_s
         UpperCamelCase_          =		None
         with self.assertRaisesRegex(
             _lowercase						,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"						,		):
             UpperCamelCase_          =		feature_extractor(_lowercase						,		padding=_lowercase						,		return_tensors="pt"     ).input_values

         UpperCamelCase_          =		feature_extractor(input_audio[0]						,		padding=_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(1, 1, 93_680)     )

         # no pad if no overlap
         UpperCamelCase_          =		2
         UpperCamelCase_          =		None
         with self.assertRaisesRegex(
             _lowercase						,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"						,		):
             UpperCamelCase_          =		feature_extractor(_lowercase						,		padding=_lowercase						,		return_tensors="pt"     ).input_values

         UpperCamelCase_          =		feature_extractor(input_audio[0]						,		padding=_lowercase						,		return_tensors="pt"     ).input_values
         self.assertEquals(truncated_outputs.shape						,		(1, 1, 93_680)     )



from __future__ import annotations

from queue import Queue




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple[int, int]:



    """simple docstring"""






    a ^= b
    b ^= a
    a ^= b
    return a, b




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list[list[int]]:



    """simple docstring"""






    UpperCamelCase_          =		1
    while (1 << j) < max_node:
        for i in range(1       ,		max_node + 1       ):
            UpperCamelCase_          =		parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    if level[u] < level[v]:
        UpperCamelCase_   ,    UpperCamelCase_          =		swap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    # making depth of u same as depth of v
    for i in range(1_8       ,		-1       ,		-1       ):
        if level[u] - (1 << i) >= level[v]:
            UpperCamelCase_          =		parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(1_8       ,		-1       ,		-1       ):
        if parent[i][u] not in [0, parent[i][v]]:
            UpperCamelCase_   ,    UpperCamelCase_          =		parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       ,		)->		tuple[list[int], list[list[int]]]:



    """simple docstring"""






    UpperCamelCase_          =		0
    UpperCamelCase_          =		Queue(maxsize=SCREAMING_SNAKE_CASE_       )
    q.put(SCREAMING_SNAKE_CASE_       )
    while q.qsize() != 0:
        UpperCamelCase_          =		q.get()
        for v in graph[u]:
            if level[v] == -1:
                UpperCamelCase_          =		level[u] + 1
                q.put(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase_          =		u
    return level, parent




def        lowerCAmelCase(	)->		None:



    """simple docstring"""






    UpperCamelCase_          =		1_3
    # initializing with 0
    UpperCamelCase_          =		[[0 for _ in range(max_node + 1_0       )] for _ in range(2_0       )]
    # initializing with -1 which means every node is unvisited
    UpperCamelCase_          =		[-1 for _ in range(max_node + 1_0       )]
    UpperCamelCase_          =		{
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 1_0],
        6: [1_1],
        7: [],
        8: [1_2, 1_3],
        9: [],
        1_0: [],
        1_1: [],
        1_2: [],
        1_3: [],
    }
    UpperCamelCase_   ,    UpperCamelCase_          =		breadth_first_search(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		1       )
    UpperCamelCase_          =		create_sparse(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("LCA of node 1 and 3 is: "       ,		lowest_common_ancestor(1       ,		3       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 5 and 6 is: "       ,		lowest_common_ancestor(5       ,		6       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 7 and 11 is: "       ,		lowest_common_ancestor(7       ,		1_1       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 6 and 7 is: "       ,		lowest_common_ancestor(6       ,		7       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 4 and 12 is: "       ,		lowest_common_ancestor(4       ,		1_2       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 8 and 8 is: "       ,		lowest_common_ancestor(8       ,		8       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )


if __name__ == "__main__":
 main()
